{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-10T04:49:08.750008Z",
     "start_time": "2025-06-10T04:48:38.748548Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import io\n",
    "import jieba\n",
    "\n",
    "# 设置数据路径\n",
    "data_dir = r'D://python-learn//JiQIXueXi\\data//THUCNews-data'\n",
    "train_path = os.path.join(data_dir, 'train.txt')\n",
    "test_path = os.path.join(data_dir, 'test.txt')\n",
    "dev_path = os.path.join(data_dir, 'dev.txt')\n",
    "\n",
    "# 下载停用词表\n",
    "print(\"下载中文停用词表...\")\n",
    "stopwords_url = \"https://raw.githubusercontent.com/stopwords-iso/stopwords-zh/master/stopwords-zh.txt\"\n",
    "response = requests.get(stopwords_url)\n",
    "stopwords = set([line.strip() for line in io.StringIO(response.text.strip())])\n",
    "print(f\"成功加载 {len(stopwords)} 个停用词\")\n",
    "\n",
    "# 字符级分词函数\n",
    "# 修改字符级分词函数\n",
    "def char_tokenize(text):\n",
    "    # 将文本按字符分割，但只过滤掉空格，不过滤所有停用词\n",
    "    # 对于中文字符级处理，大部分单字都是有意义的\n",
    "    chars = []\n",
    "    for char in text:\n",
    "        if char.strip():  # 只过滤空白字符\n",
    "            # 只过滤常见的标点和特殊符号，不过滤所有停用词\n",
    "            if char not in '，。！？；：\"\"''（）【】、':\n",
    "                chars.append(char)\n",
    "    return chars\n",
    "\n",
    "# 重新定义读取数据的函数\n",
    "def read_data(file_path):\n",
    "    texts = []\n",
    "    tokenized_texts = []\n",
    "    labels = []\n",
    "    with open(file_path, 'r', encoding='utf-8') as f:\n",
    "        for line in f:\n",
    "            try:\n",
    "                line = line.strip()\n",
    "                if not line:\n",
    "                    continue\n",
    "                # THUCNews格式通常是标签和文本以\\t分隔\n",
    "                parts = line.split('\\t')\n",
    "                if len(parts) >= 2:\n",
    "                    label = parts[0]\n",
    "                    text = parts[1]\n",
    "                    # 字符级分词\n",
    "                    tokenized_text = char_tokenize(text)\n",
    "                    # 将分词结果重新连接成字符串，用空格分隔\n",
    "                    tokenized_text_str = ' '.join(tokenized_text)\n",
    "                    \n",
    "                    # 确保分词后的文本不为空\n",
    "                    if tokenized_text_str.strip():\n",
    "                        texts.append(text)\n",
    "                        tokenized_texts.append(tokenized_text_str)\n",
    "                        labels.append(label)\n",
    "                    else:\n",
    "                        print(f\"警告: 跳过空文本，原始文本: {text[:50]}...\")\n",
    "            except Exception as e:\n",
    "                print(f\"Error processing line: {line}, error: {e}\")\n",
    "    return pd.DataFrame({'original_text': texts, 'text': tokenized_texts, 'label': labels})\n",
    "\n",
    "# 读取数据\n",
    "print(\"正在读取数据并进行字符级分词...\")\n",
    "train_df = read_data(train_path)\n",
    "test_df = read_data(test_path)\n",
    "dev_df = read_data(dev_path)\n",
    "\n",
    "# 显示数据基本信息\n",
    "print(f\"训练集大小: {len(train_df)}\")\n",
    "print(f\"测试集大小: {len(test_df)}\")\n",
    "print(f\"验证集大小: {len(dev_df)}\")\n",
    "\n",
    "# 查看类别分布\n",
    "print(\"\\n训练集类别分布:\")\n",
    "print(train_df['label'].value_counts())\n",
    "\n",
    "# 显示处理后的数据样例\n",
    "print(\"\\n字符级分词后的数据样例:\")\n",
    "print(train_df[['original_text', 'text', 'label']].head())\n",
    "\n",
    "# 标签编码和TF-IDF特征提取\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "\n",
    "# 标签编码\n",
    "label_encoder = LabelEncoder()\n",
    "train_labels_encoded = label_encoder.fit_transform(train_df['label'])\n",
    "test_labels_encoded = label_encoder.transform(test_df['label'])\n",
    "dev_labels_encoded = label_encoder.transform(dev_df['label'])\n",
    "\n",
    "print(\"\\n标签编码映射:\")\n",
    "for i, label in enumerate(label_encoder.classes_):\n",
    "    print(f\"{label} -> {i}\")\n",
    "\n",
    "# 修改TF-IDF向量化器部分\n",
    "print(\"\\n开始TF-IDF特征提取...\")\n",
    "tfidf_vectorizer = TfidfVectorizer(\n",
    "    max_features=10000,   \n",
    "    min_df=2,             \n",
    "    max_df=0.95,          \n",
    "    ngram_range=(1, 3),   \n",
    "    analyzer='char'       # 只用字符级分析，不需要token_pattern\n",
    ")\n",
    "\n",
    "\n",
    "# 在训练集上拟合并转换\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(train_df['text'])\n",
    "X_test_tfidf = tfidf_vectorizer.transform(test_df['text'])\n",
    "X_dev_tfidf = tfidf_vectorizer.transform(dev_df['text'])\n",
    "\n",
    "# 打印特征维度信息\n",
    "print(f\"TF-IDF特征维度: {X_train_tfidf.shape[1]}\")\n",
    "print(f\"训练集样本数: {X_train_tfidf.shape[0]}\")\n",
    "print(f\"测试集样本数: {X_test_tfidf.shape[0]}\")\n",
    "print(f\"验证集样本数: {X_dev_tfidf.shape[0]}\")\n",
    "\n",
    "# 查看特征稀疏度\n",
    "print(f\"训练集稀疏度: {X_train_tfidf.nnz / (X_train_tfidf.shape[0] * X_train_tfidf.shape[1]):.6f}\")\n",
    "\n",
    "# 获取最重要的特征\n",
    "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
    "print(\"\\nTF-IDF特征示例:\")\n",
    "for i in range(min(10, len(feature_names))):\n",
    "    print(feature_names[i])\n",
    "\n",
    "# 使用PCA或TruncatedSVD降维(对于稀疏矩阵，使用TruncatedSVD)\n",
    "print(\"\\n执行PCA降维...\")\n",
    "n_components = 200  # 降维后的特征数量\n",
    "svd = TruncatedSVD(n_components=n_components, random_state=42)\n",
    "X_train_pca = svd.fit_transform(X_train_tfidf)\n",
    "X_test_pca = svd.transform(X_test_tfidf)\n",
    "X_dev_pca = svd.transform(X_dev_tfidf)\n",
    "\n",
    "# 打印降维信息\n",
    "print(f\"PCA降维后特征维度: {X_train_pca.shape[1]}\")\n",
    "print(f\"解释方差比例: {np.sum(svd.explained_variance_ratio_):.4f}\")\n",
    "\n",
    "# 可视化累积解释方差\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(np.cumsum(svd.explained_variance_ratio_))\n",
    "plt.xlabel('PCA特征数量')\n",
    "plt.ylabel('累积解释方差比例')\n",
    "plt.title('PCA累积解释方差')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 显示一些样本在降维后空间中的分布\n",
    "plt.figure(figsize=(12, 10))\n",
    "colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'orange', 'purple', 'brown']\n",
    "markers = ['o', 's', '^', 'v', '<', '>', 'p', '*', 'h', 'H']\n",
    "\n",
    "# 选择部分类别和样本以便可视化\n",
    "sample_size = 300\n",
    "selected_indices = []\n",
    "for label in range(len(label_encoder.classes_)):\n",
    "    indices = np.where(train_labels_encoded == label)[0]\n",
    "    if len(indices) > 0:\n",
    "        selected_indices.extend(np.random.choice(indices, min(30, len(indices)), replace=False))\n",
    "\n",
    "# 绘制散点图\n",
    "for i, label in enumerate(label_encoder.classes_):\n",
    "    idx = np.where(train_labels_encoded[selected_indices] == i)[0]\n",
    "    if len(idx) > 0:\n",
    "        plt.scatter(\n",
    "            X_train_pca[selected_indices][idx, 0], \n",
    "            X_train_pca[selected_indices][idx, 1],\n",
    "            c=colors[i % len(colors)], \n",
    "            marker=markers[i % len(markers)],\n",
    "            label=label,\n",
    "            alpha=0.7\n",
    "        )\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.title('The data distribution after PCA dimensionality reduction')#PCA降维后的数据分布\n",
    "plt.xlabel('Principal Component 1')#主成分1\n",
    "plt.ylabel('Principal Component 2')#主成分2\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下载中文停用词表...\n",
      "成功加载 794 个停用词\n",
      "正在读取数据并进行字符级分词...\n",
      "训练集大小: 200000\n",
      "测试集大小: 19999\n",
      "验证集大小: 19991\n",
      "\n",
      "训练集类别分布:\n",
      "label\n",
      "体育    20000\n",
      "娱乐    20000\n",
      "家居    20000\n",
      "教育    20000\n",
      "时政    20000\n",
      "游戏    20000\n",
      "社会    20000\n",
      "科技    20000\n",
      "股票    20000\n",
      "财经    20000\n",
      "Name: count, dtype: int64\n",
      "\n",
      "字符级分词后的数据样例:\n",
      "               original_text                                             text  \\\n",
      "0  国字号主帅年收入将达50万 有望直接签到2014年  国 字 号 主 帅 年 收 入 将 达 5 0 万 有 望 直 接 签 到 2 0 1 4 年   \n",
      "1   姚明退役令易建联震惊 7月18日正式和国家队汇合    姚 明 退 役 令 易 建 联 震 惊 7 月 1 8 日 正 式 和 国 家 队 汇 合   \n",
      "2   国青小将染红或遭洋帅清洗 后防核心无缘亚青赛首战    国 青 小 将 染 红 或 遭 洋 帅 清 洗 后 防 核 心 无 缘 亚 青 赛 首 战   \n",
      "3    七年一轮回再碰马刺 巴蒂尔：离开火箭至今难释怀        七 年 一 轮 回 再 碰 马 刺 巴 蒂 尔 离 开 火 箭 至 今 难 释 怀   \n",
      "4   小布脚踝扭伤进观察名单 西部第8之争休城先输一手    小 布 脚 踝 扭 伤 进 观 察 名 单 西 部 第 8 之 争 休 城 先 输 一 手   \n",
      "\n",
      "  label  \n",
      "0    体育  \n",
      "1    体育  \n",
      "2    体育  \n",
      "3    体育  \n",
      "4    体育  \n",
      "\n",
      "标签编码映射:\n",
      "体育 -> 0\n",
      "娱乐 -> 1\n",
      "家居 -> 2\n",
      "教育 -> 3\n",
      "时政 -> 4\n",
      "游戏 -> 5\n",
      "社会 -> 6\n",
      "科技 -> 7\n",
      "股票 -> 8\n",
      "财经 -> 9\n",
      "\n",
      "开始TF-IDF特征提取...\n",
      "TF-IDF特征维度: 10000\n",
      "训练集样本数: 200000\n",
      "测试集样本数: 19999\n",
      "验证集样本数: 19991\n",
      "训练集稀疏度: 0.006971\n",
      "\n",
      "TF-IDF特征示例:\n",
      " !\n",
      " ! \n",
      " %\n",
      " % \n",
      " &\n",
      " & \n",
      " (\n",
      " ( \n",
      " )\n",
      " ) \n",
      "\n",
      "执行PCA降维...\n",
      "PCA降维后特征维度: 200\n",
      "解释方差比例: 0.3342\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29305 (\\N{CJK UNIFIED IDEOGRAPH-7279}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24449 (\\N{CJK UNIFIED IDEOGRAPH-5F81}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32047 (\\N{CJK UNIFIED IDEOGRAPH-7D2F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35299 (\\N{CJK UNIFIED IDEOGRAPH-89E3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 37322 (\\N{CJK UNIFIED IDEOGRAPH-91CA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26041 (\\N{CJK UNIFIED IDEOGRAPH-65B9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24046 (\\N{CJK UNIFIED IDEOGRAPH-5DEE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20363 (\\N{CJK UNIFIED IDEOGRAPH-4F8B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAIdCAYAAADcaPcJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABocUlEQVR4nO3deXhU5f3+8XuyT0IWIBtLQiCENUBCwi4KIiKooIDaurRWpJZYka9I0VZbd21ttcXtZ92wSN0QFBQFF0BEISCLbIGEHQJZCSF7JnN+f0SiKesoJ2dm8n5dF9fFPOdk5jMfD+PcOec8j80wDEMAAAAAgHPiY3UBAAAAAOBJCFEAAAAA4AJCFAAAAAC4gBAFAAAAAC4gRAEAAACACwhRAAAAAOACQhQAAAAAuIAQBQAAAAAuIEQBAAAAgAv8rC4AAOBZbrrpJmVmZjYa8/f3V2RkpIYPH65p06YpPDy8YduePXv0+uuv66uvvlJ+fr5atWql1NRU3XbbberWrdspX2PevHn605/+pKFDh+rll1829f0AAOAqQhQAwGU9evTQX/7yl4bHtbW12rp1q5566ilt375db775pmw2mz799FPNmDFDSUlJmjJlitq3b68jR45ozpw5uuaaa/Tcc8/pwgsvPOn533vvPXXp0kWrVq3SgQMHFBcXd9I+K1as0OrVq09bY1hYmKZMmdLw2Ol06m9/+5tsNttpf2b06NHq3bu3170GAOD8IkQBAFzWokULpaSkNBrr16+fysvLNWvWLG3atEmtWrXSH/7wBw0dOlT//Oc/5evr27DvqFGjdP311+uee+7RF198oaCgoIZte/bs0fr16/Xvf/9bM2bM0DvvvKPp06efVIO/v79mzpx52hq//vrrRo+dTqeGDx+uAQMGnPPPeMtrAADOL+6JAgCcN8nJyZKk3NxczZkzRzU1NbrvvvsaBShJCgoK0syZMzVx4kSVlpY22vbee+8pNDRUgwYN0mWXXab33ntPNTU1TfYeAAA4G0IUAOC82bNnjyQpLi5OK1euVI8ePRQTE3PKfQcMGKC77rpL0dHRDWN1dXX64IMPNGbMGAUEBGj8+PEqKirSZ5991iT1AwBwLghRAACXGYYhh8PR8KeoqEgff/yxXnjhBaWkpCg5OVl5eXlq3769S8/75ZdfKj8/XxMmTJAkpaSkqHPnznrzzTfNeBsAAPwk3BMFAHDZ2rVr1bNnz0ZjPj4+GjRokB5++GHZbDbZbDbV1dW59LzvvfeeOnTooI4dOzZc5jd69Gg988wz2rVrlxITE8/bewAA4KciRAEAXNazZ089+OCDkiSbzabAwEC1adNGLVq0aNinXbt2ys3NPe1zOBwOFRcXN1zOV1xcrOXLl6u2tlb9+vU7af+3335bf/zjH8/zOwEAwHWEKACAy0JCQtSrV68z7nPBBRfo9ddfV0FBgaKiok7avnLlSv3ud7/TU089pcsvv1wffPCBamtr9eyzzyosLKzRvs8995zef/993XXXXY1m8gMAwArcEwUAMMUNN9wgf39/PfLIIydd1ldZWalZs2YpPDxcw4cPlyTNnz9fKSkpGjlypAYMGNDozy9/+UsdO3ZMH3/8sRVvBQCARghRAABTtG/fXg888IA+/fRT3XDDDXr//fe1Zs0avfvuu5o4caKys7P1r3/9S8HBwfruu++0c+dOXX755ad8rhEjRig8PFxvvfVWE78LAABOxuV8AADTXH311erQoYNef/11/fOf/1RRUZGioqKUmpqqf/3rX+rcubOk+gklfH19NWbMmFM+T0BAgEaPHq233npL27dvV/fu3bV//36tXLnytK9dXl6uwYMHNxp78803tXz58tP+zP8uIOwtrwEAOL9shmEYVhcBAAAAAJ6Cy/kAAAAAwAWEKAAAAABwASEKAAAAAFxAiAIAAAAAF1gyO19RUZHuv/9+ZWZmytfXV2PHjtXMmTPl59e4HKfTqeeee07z5s1TaWmp2rdvrylTpjTM3uR0OpWWlibDMGSz2Rp+btWqVQoODm7S9wQAAACgebAkRE2bNk0xMTFauXKlCgsLNWXKFM2ePVu33npro/3mzp2r999/X3PmzFF8fLyWLVumjIwMJScnKz4+Xjk5OaqtrdX69esVEBBgxVsBAAAA0Mw0+eV8+/btU2ZmpmbMmCG73a64uDhlZGRo7ty5J+17ww03aNGiRYqPj1dNTY2Ki4tlt9sVFBQkSdq8ebO6du1KgAIAAADQZJr8TFR2drYiIiIUExPTMJaYmKjc3FyVlpYqLCysYdzHx0fBwcH66quvNHnyZBmGoXvvvVfR0dGS6kNUdXW1JkyYoEOHDikxMVHTp09X3759m/ptAQAAAGgmmjxElZeXy263Nxo78biioqJRiDqhf//+2rx5s9auXauMjAxFRUVpzJgxCgoKUu/evXXnnXcqPDxcc+fO1aRJk7Rw4ULFxcWdc03FxcflDksO22xSq1ahblOPt6G/5qPH5qK/5qPH5qK/5qPH5qK/5rOyxyde+1w0eYgKDg5WZWVlo7ETj0NCQk75Mycu1xs0aJDGjRunRYsWacyYMbrnnnsa7Tdp0iTNnz9fK1as0I033njONTmdcot/CCfmxnCXerwN/TUfPTYX/TUfPTYX/TUfPTYX/TWflT3+0Tx1Z9Xk90QlJSWppKREhYWFDWO7du1SbGysQkMbJ78nnnhCTzzxRKOxmpoaRURESJKefvppbdu27aTtgYGB5hQPAAAAoNlr8hCVkJCgtLQ0PfbYYyorK9OBAwf0/PPPa+LEiSftm56errfeektr166V0+nUF198ocWLF+uaa66RJO3cuVOPPvqoCgoKVFNTo2effVZlZWUaOXJkU78tAAAAAM2EJYvtzpo1Sw6HQyNGjNC1116roUOHKiMjQ5KUmpqqhQsXSpIuueQS3XfffbrvvvvUr18/Pffcc3rmmWcaJo54/PHHFR8fr3HjxmnAgAHKzMzUa6+91nCmCgAAAADON5thcEVnYaF73Bxos0mRkaFuU4+3ob/mo8fmor/mo8fmor/mo8fmor/ms7LHJ177XFhyJgoAAAAAPBUhCgAAAABcQIgCAAAAABcQogAAAADABYQoAAAAAHABIQoAAAAAXECIAgAAAAAXEKIAAAAAwAWEKAAAAABwASEKAAAAAFxAiAIAAAAAFxCiAAAAAMAFhCgAAAAAlthXXKGcwnKry3CZn9UFAAAAAGg+9h+t1Gc7CvTZzgJlF5TL1yYtnDxA0aGBVpd2zghRAAAAAEx1sOREcCrUjvyyhnFfH5su6RKpCLu/hdW5jhAFAAAA4LzLPValz3cW6NMdBdqe96PgZJP6xbfUyK5Ruqhza4V7WICSCFEAAAAAzpP849X6bGeBlmYVaOuR4w3jPjYpPS5Cl3SN0vDOkYoI9rzg9GOEKAAAAAA/2dGKGn2+s1BLdxRo48FjMr4f97FJfduH1wenpEi1Cg6wtM7ziRAFAAAAwCVl1Q4ty64PTmv3HVWd8cO2lHZhGtk1Shd3iVJkiPcEpx8jRAEAAAA4q8raOq3cVaRPdxRo1Z5i1f4oOXWPaaGRXaM0smuUYsOCLKyyaRCiAAAAAJxSjcOpb/Ye1ac78vXlriJV1jobtnVsFaxLu9UHpw6tgi2ssukRogAAAAA0cDgNfbu/REt35GtZdpGOVzsatrUND9KlXaN0abcodY4Mkc1ms7BS6xCiAAAAgGbOaRj67lCplu4o0Oc7C1RcUduwLTIkQCO7RmlUtyj1iA1ttsHpxwhRAAAAQDNkGIay8su0NKt+Lae849UN28KD/HTJ9/c4pbQLl68PwenHCFEAAABAM7K7qFxLsgr0aVa+DpRUNYyHBPhqWFKkLu0apf7xEfLz9bGwSvdGiAIAAAC83JHSKn26o0Afb89XdkF5w3ign4+GdmqtS7tFaXDHVgr0IzidC0IUAAAA4IWOVdbqi+xCfbI9Xxt+tAiur49NgxJaalS3aA1NbKWQACKBq+gYAAAA4CWqauv01e5ifbI9X6v2FMvh/GEtp9R2Ybqse7Qu7hKlCLu/hVV6PkIUAAAA4MFOTEn+cVa+lmcXqrymrmFbUlSIRnWL1qhuzWMR3KZCiAIAAAA8jGEY2pZXpk+252tpVn6jKcljQwM1qnu0Luserc6RIRZW6b0IUQAAAICH2H+0Up9sz9OSrALtP1rZMH5iSvLLukWrd7sw+bCWk6kIUQAAAIAbKyyv0dKsfC3JKtC2I8cbxgP9fHRRYmtd1j1aAxNayp8pyZsMIQoAAABwMxU1dVqWXaiPt+dp7f4SnZgfwtcm9e/QUpd1j9awzpEKDvC1ttBmihAFAAAAuAGH09Da/Ue1eFv9BBFVDmfDtl5tQnVZ92hd0jVKrYIDLKwSEiEKAAAAsIxhGNqZX67F39/nVFRe07AtvqVdo7+fIKJ9hN3CKvG/CFEAAABAE8s7Xq1Ptudr8bY87S6qaBgPD/LTpd2iNaZHtHrGhsrGBBFuiRAFAAAANIHyGoe+2Fmoxdvz9e3+Ep1YBjfA16ahia01unuMBndkgghPQIgCAAAATOJwGlqz76g+3pan5TlFqv7RfU6p7cI0ukeMLukSpdAgvpZ7Ev5rAQAAAOeRYRjakV+mxdvyteR/FsLt0NKuMT1idFn3aLUND7KwSvwchCgAAADgPDhSWlV/n9P2fO350X1OEXZ/Xdo1SmN6xqhHTAvuc/IChCgAAADgJyqrPnGfU57WHzjW6D6nCxNba3SPGA1OaCk/7nPyKoQoAAAAwAV1TkOr9x7VR1vztGJX4/uc+rYP15ge0bo4ifucvBn/ZQEAAIBzsKeoQi+vPaT3vj2ggrIf1nNKaPXDfU5twrjPqTkgRAEAAACnUVpVq6VZBfpwa562HjneMB4e5KdR3aJ1ec8Ydec+p2aHEAUAAAD8iMNpaM3eo/pw6xGt2FWk2rr6O518bdLwbtG6NClSQzq2UoAf9zk1V4QoAAAAQNKuwnJ9tDVPi7fnq6j8h8v1OkeG6Mrk+sv1unZorcLC4zKMMzwRvB4hCgAAAM1WSeWJy/WOaHteWcN4hN1fl3WP1hU9YtQlOkQ2m01csYcTCFEAAABoVhxOQ9/sKdaHW/O0cvePLtfzsemCjq10Rc8YDenUSv5MS47TIEQBAACgWcgpKNeHW/P08fY8FVfUNox3iQrRFcmxuqxblFoGB1hYITwFIQoAAABeq6SiVkuy8vXh1jxl5f9wuV5Lu79G94jW5T1i1CW6hYUVwhMRogAAAOBVHHVOrdpTP7veV7uL5XDWX67n52PT0MTWurxHjIZ0bCk/LtfDT0SIAgAAgFfILiirv1xvW76OVv5wuV636Ba6omeMRnWLVkSwv4UVwlsQogAAAOCxjlXWaklWgRZtOdLocr1Wwf4a3T1GV/SMUeeoEAsrhDciRAEAAMCj1DkNrdtfooVbjmh5TqFq6n64XO+izvWX6w1K4HI9mMeSEFVUVKT7779fmZmZ8vX11dixYzVz5kz5+TUux+l06rnnntO8efNUWlqq9u3ba8qUKRozZkzDPi+99JLmzJmj0tJS9erVSw8++KA6derU1G8JAAAAJjt0rFIfbsnTh1vzdOR4dcN4UlSIxibH6jIu10MTsSRETZs2TTExMVq5cqUKCws1ZcoUzZ49W7feemuj/ebOnav3339fc+bMUXx8vJYtW6aMjAwlJycrPj5eCxYs0Jw5c/TKK68oPj5eTz/9tKZOnapFixbJxmpoAAAAHq+qtk7Lcgq1cEue1u0vaRgPDfTTZd2jNTY5Rl2jW/DdD02qyUPUvn37lJmZqS+//FJ2u11xcXHKyMjQk08+eVKIuuGGGzRhwgQFBwerpqZGxcXFstvtCgoKkiS98847uv7665WUlCRJmj59ut555x2tWbNGAwcObOq3BgAAgPPAMAxtyyvToi1HtCQrX2XVdZIkm6T+HSI0NjlWF3WOVKAfl+vBGk0eorKzsxUREaGYmJiGscTEROXm5qq0tFRhYWEN4z4+PgoODtZXX32lyZMnyzAM3XvvvYqOjpYk5eTkaPLkyQ37+/v7KyEhQVlZWS6FKHf5xcWJOtylHm9Df81Hj81Ff81Hj81Ff83n6T0+WlGjxdvytXDLEe0qrGgYbxsWqCuTY3VFzxi1CQ+yrD5P768nsLLHrrxmk4eo8vJy2e32RmMnHldUVDQKUSf0799fmzdv1tq1a5WRkaGoqCiNGTPmlM8VFBSkioqKk57jTFq3DnXxXZjL3erxNvTXfPTYXPTXfPTYXPTXfJ7UY0edU19mF+idtQf12fa8hjWdAv18NDo5Vtemx2lgp9by8XGf5OJJ/fVU7t7jJg9RwcHBqqysbDR24nFIyKmnnwwICJAkDRo0SOPGjdOiRYs0ZswY2e12VVVVNdq3qqrqtM9zOkVFx2UYLv2IKWy2+gPGXerxNvTXfPTYXPTXfPTYXPTXfJ7U433FFVr0/SQRheU1DeM9YkM1Nrl+TafQoPqvqsXFZad7miblSf31VFb2+MRrn4smD1FJSUkqKSlRYWGhIiMjJUm7du1SbGysQkMbF/3EE09Iku65556GsZqaGkVERDQ8V3Z2toYPHy5Jqq2t1d69e9WlSxeXajIMudU/BHerx9vQX/PRY3PRX/PRY3PRX/O5a48raur02c76NZ02HiptGA8P8tOYHjEamxzbaE0nd3wPkvv215u4e4+bPEQlJCQoLS1Njz32mB566CEdPXpUzz//vCZOnHjSvunp6br77rs1YsQIpaWlafny5Vq8eLFeffVVSdKECRP0zDPP6MILL1THjh319NNPKzIyUunp6U39tgAAAHAKhmFo8+Hj+mDzYX26o0CVtU5Jko9NGpTQSmOTYzQ0sbX8WdMJHsSSKc5nzZqlhx56SCNGjJCPj4+uuuoqZWRkSJJSU1P14IMPauzYsbrkkkt033336b777lNhYaESEhL0zDPPqG/fvpKkiRMn6vjx47r99ttVXFysXr166cUXX5S/P+sDAAAAWKmkslaLt+Xpg81HtLvoh/vV4yKCdGVyrC7vEaPo0EALKwR+OpthuPOJsqZRWOge17XabFJkZKjb1ONt6K/56LG56K/56LG56K/5rO6x0zC0bn+JPth8RMtyClVb98MkEZd0jdLY5Biltgv32DWdrO5vc2Blj0+89rmw5EwUAAAAvEdhWbUWba0/63To2A+TfnWNbqGresU2miQC8AYczQAAAHCZw2nomz3F+mDzEX21u0jfn3RSSICvLuserXG9YtU9xr2nqQZ+KkIUAAAAzlnusSp9sOWIPtxyRPllP0xN3rttmK7qFatLukbJ7u9rYYWA+QhRAAAAOKPaOqdW5BTp/c2HlbmvRCduVQkP8tPlPWM0rlesOrV2bZ1OwJMRogAAAHBKe4sq9P7mI/poW55KKmsbxvvHR2hcr1gN6xypAD+mJkfzQ4gCAABAg6raOn2+s1Dvbz7caEHcyJAAjU2O0ZXJsWofYbewQsB6hCgAAABoR16Z3t98WJ9k5ausuk5S/YK4Qzq20rhebTSkUyv5+Xjm1OTA+UaIAgAAaKbKqh1ampWv9zcf0fa8sobxtmGBGterja7oyYK4wKkQogAAAJoRwzC0+fBxvf/dYX26o0BVDqckyc/HpmGdI3VV71j1i4+Qj4cuiAs0BUIUAABAM1BW7dDibfla8N1h5RSWN4wntLLrql5tNKZHtFoGB1hYIeA5CFEAAABeyjAMbcsr04JNh7UkK7/hrFOgn48u6RKpq3u3Ue+2YbJx1glwCSEKAADAy5TXOPTJ9gLN33RYO/J/uNepY6tgXd2njS7vEa2wIH8LKwQ8GyEKAADAS2TllWnxyr16f/0hVdTWz7AX4GvTxV2iNL53G6W046wTcD4QogAAADxYZW2dPs0q0HvfHda2I8cbxuNb2jW+dxtd3jNGEXbOOgHnEyEKAADAA+UUlGv+d4e1eFueymvqzzr5+dh0WXKsrugWpb7twznrBJiEEAUAAOAhqmrr9PnOQr236bA2Hy5tGG8XHqTxvdvoyuQYdenQWoWFx2UYFhYKeDlCFAAAgJvbU1TRcNaptMohSfL1semixNYa37uN+nWoX9eJE09A0yBEAQAAuKEah1NfZBdq/qZcbTj0w1mntmGBuqp3G13ZM0aRLQItrBBovghRAAAAbmT/0UrN33RYH249omMnzjrZpAs6tdb4Pm00oENL+fpwygmwEiEKAADAYg6noVW7izRv42Gt3ne0YTy6RYCu6t1G45JjFR3KWSfAXRCiAAAALFJYXqOFm49o/neHlXe8WpJkkzS4YyuN79NGgzu2kh9nnQC3Q4gCAABoQoZhaOOhUs3bmKsvsgvlcNZPoxce5KdxvdpofJ9YtQu3W1wlgDMhRAEAADSBsmqHPt6er/c25WpXYUXDeK82YZqY0kYjukQp0M/HwgoBnCtCFAAAgIlyCso1b1OuPt6Wr4ra+kVxg/x8dFn3aE3s01ZdY1pYXCEAVxGiAAAAzrPaOqeWZRdq3sbG05MntLJrQp+2urxHjEKD+BoGeCr+9QIAAJwnR0qrtOC7w3p/8xEVV9RKqp+efFhSpCb2aau0uHDZWBEX8HiEKAAAgJ/BaRhas++o5m08rK92F+n7eSIU1SJAV/dqo3G9mJ4c8DaEKAAAgJ+gtKpWi7bkad6mXB0sqWoYT4+P0DV92ujCxNby82WiCMAbEaIAAABckF1Qpnc31k8UUeVwSpJCAnx1Rc8YTejTVh1bB1tcIQCzEaIAAADOwlHn1PKcIr2zMVcbDh5rGO8cGaJrUttqdPdo2f19LawQQFMiRAEAAJxGUXmN3t98WPM3HVZ+WY2k+okihidF6prUtkptx0QRQHNEiAIAAPgRwzC05fBxvbMxV5/tKJDj+5kiWgX76+rebTS+dxsmigCaOUIUAACApGqHU5/uyNc7G3K1Pa+sYbxXm1Bdk9pWI5KiFODHRBEACFEAAKCZO1JapXmbDuv97w7rWJVDkhTga9Ol3aJ1TUpb9YgNtbhCAO6GEAUAAJodwzC07kCJ3tmQqy93/bC2U0xooCb2qV/bqWVwgLVFAnBbhCgAANBsVNTU6aNteXp3Q672FFc0jPeLj9C1KW11QWJr+fkwUQSAMyNEAQAAr7e3uELzNubqw615Kq+pkyTZ/X10eY8YXZPaVp1ah1hcIQBPQogCAABeqc5p6Os9xXpnQ65W7zvaMB7f0q5rU9rq8p4xahHIVyEAruOTAwAAeJWyaocWbjmidzbk6tCxKkmSTdIFnVrp2tS26t+hpXxY2wnAz0CIAgAAXmH/0Uq9s+GQFm3JU0Vt/SV7oYF+GtcrVhNT2qhduN3iCgF4C0IUAADwWIZhaM2+o3prfa5W7SluGO/YOli/SG2r0T1iZPf3tbBCAN6IEAUAADxOZW2dFm/L09vrf5hlzyZpSKdW+kVqO/XvECEbl+wBMAkhCgAAeIzDpVV6Z0OuPth8RMer6xfGDQnw1RU9Y3RdajvFteSSPQDmI0QBAAC3ZhiGNhw6prfW52pFTmHDwrjtI4J0bWo7XcksewCaGJ84AADALVU7nFqala+31h/SzoLyhvF+8RH6Rd92GtKxlXxZGBeABQhRAADArRSUVWvepsNasOmwjlbWSpIC/Xw0pke0rkttp8RIFsYFYC1CFAAAcAtbD5fqzfWH9NnOQtV9f81eTGigrklpq6t6xSrc7m9xhQBQjxAFAAAs46hz6ovsQr21/pA2Hz7eMJ7SLky/6NtOF3WOlB+X7AFwM4QoAADQ5Eoqa7Xgu8N6d2OuCspqJEn+vjZd2jVK1/Vtp+4xoRZXCACnR4gCAABNZldBmZ7/LFsfbs1TtcMpSWoV7K+JfdpqfJ82ah0SYHGFAHB2hCgAAGAqwzD07YFj+u+3B7Vyd3HDeLfoFvplWjuN7Bolf18fCysEANcQogAAgClq65z6dEeB5q472DBFuc0mDe3UWtentVPf9uGy2bjfCYDnIUQBAIDz6lhlreZ/d1jvbMhVYXn9/U6Bfj4amxyjKSO6KMxmyDAsLhIAfgZCFAAAOC/2FVforfWH9OHWPFV9f79TZEiArk1tq/G92ygi2F+RkS1UWHj8LM8EAO7NkhBVVFSk+++/X5mZmfL19dXYsWM1c+ZM+fmdXM6bb76p2bNnKz8/X9HR0frVr36lG264QZLkdDqVlpYmwzAaXQ6watUqBQcHN9n7AQCguTIMQ+sPHtN/vz2klbuKdOIEU5eoEN2Q3p77nQB4JUtC1LRp0xQTE6OVK1eqsLBQU6ZM0ezZs3Xrrbc22u+zzz7TU089pZdeekl9+vTRxo0b9dvf/laRkZEaNWqUcnJyVFtbq/Xr1ysggNl8AABoKrV1Tn22s0D/XXdIWfllDeNDO7XS9WntlRbH/U4AvFeTh6h9+/YpMzNTX375pex2u+Li4pSRkaEnn3zypBCVl5enyZMnKyUlRZKUmpqqAQMGaO3atRo1apQ2b96srl27EqAAAGgipVW1WvDdEb2z4ZDyy3643+mKnjH6Rd92SmjFlSAAvF+Th6js7GxFREQoJiamYSwxMVG5ubkqLS1VWFhYw/iJy/ZOKCoq0tq1a3XvvfdKkjZv3qzq6mpNmDBBhw4dUmJioqZPn66+ffu6VJO7/KLsRB3uUo+3ob/mo8fmor/mo8end+Bopd5cf0gLNx9puN+p9ff3O034/n6ns6G/5qPH5qK/5rOyx668ZpOHqPLyctnt9kZjJx5XVFQ0ClE/VlBQoNtuu03Jycm64oorJElBQUHq3bu37rzzToWHh2vu3LmaNGmSFi5cqLi4uHOuqXVr91oV3d3q8Tb013z02Fz013z0uJ5hGMrcU6xXvtqjT7fnNcyo1y02VLcO7aQr+7RRoJ+vy89Lf81Hj81Ff83n7j1u8hAVHBysysrKRmMnHoeEhJzyZzZu3Kg777xT6enpevzxxxsmoLjnnnsa7Tdp0iTNnz9fK1as0I033njONRUVHXeLqVZttvoDxl3q8Tb013z02Fz013z0uJ7DaeizHQX677qD2pb3w/1OQzq20g3p7dQvPkI2m03HSyrkyjx79Nd89Nhc9Nd8Vvb4xGufiyYPUUlJSSopKVFhYaEiIyMlSbt27VJsbKxCQ08uet68eXrkkUc0depU3XLLLY22Pf300xo1apR69OjRMFZTU6PAwECXajIMudU/BHerx9vQX/PRY3PRX/M11x5X1NTp/c2H9ea3h3TkeLWk+vudxvSI1i/7tlfH1j/c7/Rz+tNc+9uU6LG56K/53L3HTR6iEhISlJaWpscee0wPPfSQjh49queff14TJ048ad8lS5bogQce0AsvvKChQ4eetH3nzp1at26d/vnPfyo8PFz//ve/VVZWppEjRzbFWwEAwCsUltfo7fWH9N6mwzpe7ZAktbT765rUtprYp41aBjOBEwD8mCVTnM+aNUsPPfSQRowYIR8fH1111VXKyMiQVD8D34MPPqixY8fq2WefVV1dnaZOndro56+88ko99NBDevzxx/XXv/5V48aNU2VlpXr16qXXXntNERERFrwrAAA8y96iCr2x7qAWb89TbV39r3zjW9p1Q1o7jekRoyB/1+93AoDmwGYY7nyirGkUFrrHda02mxQZGeo29Xgb+ms+emwu+mu+5tBjwzC08VCp5qw9oJW7ixvGe7cN003p7XVh59byMWlarObQX6vRY3PRX/NZ2eMTr30uLDkTBQAAmlad09CKnELNWXdQWw7XTwdhk3RhYmvd1K+9+rQLt7ZAAPAghCgAALxYVW2dPtyap/9+e1AHSqokSQG+No3pEaMb0tuzOC4A/ASEKAAAvFBJRa3e3ZirdzbmqqSyVpIUFuSniSltdW1KW7UOYbIIAPipCFEAAHiRgyWVmrvuoBZtzVO1wylJahsWqOvT2mtsr1jZmSwCAH42QhQAAF5g6+FSzVl3UF/sLNSJe7G7x7TQjentdXGXKPn5mDNZBAA0R4QoAAA8lNMwtGp3seasO6gNB481jA/u2FI3pccpLS5cNpNm2gOA5owQBQCAh6lxOPXJ9ny9se6g9hRXSJL8fGwa1T1aN6a3V+fIEIsrBADvRogCAMBDlFU7tOC7w/rvt4dUWF4jSQoJ8NX43m10Xd92igkNtLhCAGgeCFEAALi54ooavb3+kN7deFjHqx2SpOgWAfpF33a6uncbtQjkf+cA0JT41AUAwE3lHqvSG+sOauGWIw0z7XVoadev+sdpdPdo+fv6WFwhADRPhCgAANxMTkG5Xl97QJ9m5avu+6n2esSG6ub+cbqoc2v5MFkEAFiKEAUAgJvYdOiYZmce0Fe7ixvGBnSI0K/7xyk9LoKZ9gDATRCiAACwkGEYWrWnWK9nHtDGQ6WSJJukEV0i9av+ceoeE2ptgQCAkxCiAACwgMNp6NMd+fpP5kHlFJZLkvx9bbq8R4xu6hen+JZ2iysEAJwOIQoAgCZUVVunRVvz9MbaA8otrZYkBfv7akKfNvplWjtFtWCacgBwd4QoAACawPEqh+ZtytWb3x7S0cpaSVJLu79+0bedJqa0UViQv8UVAgDOFSEKAAATFZRV681vD2n+d4dVXlMnSWoTFqgb0+M0NjlGQf6+FlcIAHAVIQoAABPsP1qpOWsP6KNtear9fp7yxMhg/bp/nEZ2iZIfazwBgMciRAEAcB7tyCvT7MwD+iK7QM7v13jq0zZMNw+I05COrZimHAC8ACEKAIDzYNOhY3ptzQGt2vPDGk8XdGqlX/eLU0r7cAsrAwCcb4QoAAB+IsMwlLm/RK+t2a9vDxyTJPnYpJFdo/Tr/nFKimphcYUAADMQogAAcJHTMLRyV7FeW7NfW48clyT5+dh0ec8Y/bpfnOJY4wkAvBohCgCAc1TnNPT5zgK9tuZAwwK5gX4+uqpXrG5Mb6/YsCCLKwQANAVCFAAAZ1Fb59TH2/L1+toD2n+0UpIUEuCriSltdX1aO7UKDrC4QgBAUyJEAQBwGlW1dVq45Yj+s/ag8o5XS5LCg/x0Xd92ui61LQvkAkAzRYgCAOB/lNc4NG/DYc399qCKK2olSa1DAnRDWjtN6NNWwQEskAsAzRkhCgCA7x2rrNWcT3fqtVV7VFrlkCTFhgbqV/3jNDY5VoF+LJALACBEAQCgoxU1mvvtIb27IVcVtXWSpPiWdt3cP06ju0fLz5fwBAD4ASEKANBsFZXX6I11BzVvY66qHE5JUrfYUP06vb2GJ0XK18dmcYUAAHdEiAIANDuFZdWas+6g3tt0WNXfh6fuMS1066AOGj+gg4qKymQYFhcJAHBbhCgAQLORf7xa/1l7QAu+O6yauvqU1DM2VJMHddDgji3l42OTzcbZJwDAmRGiAABe70hplV7PPKAPthxR7ffhqVebME0eHK+BHVoSnAAALiFEAQC8Vu6xKs3O3K9FW/LkcNaHp9R2Ybp1UAf1i48gPAEAfhJCFADA6xwsqdRra/bro235qvs+PKXHhevWQR2UFhdhbXEAAI9HiAIAeI39Ryv16pr9+mRbnr6/ak8DOkRo0sAOSm0fbm1xAACvQYgCAHi8vUUVenXNfi3Jytf3J540KKGlbh3UQb3bhllbHADA6xCiAAAea1dhuV5dvV+f7ijQiRnJL+jUSrcOjFfPNoQnAIA5CFEAAI+TU1iuV77Zp893FjaEp2GdW2vSwHh1iwm1tDYAgPcjRAEAPMbuonK99PV+fbazoGHs4qRI3TIwXl2jW1hYGQCgOSFEAQDc3t6iCr28ep+WZv1w2d4lXSI1aVAHdY4MsbQ2AEDzQ4gCALit/Ucr9fI3+xpNGDE8KVKTB8UrKYozTwAAaxCiAABu52BJpV5e3Xiq8osSW2vy4A5ctgcAsBwhCgDgNg4dq9Srq/fro60/hKcLOrXSbwd3UHcmjAAAuAlCFADAcodLq/Tq6v1atDVPdd9ftze4Y0v9dlAHpioHALgdQhQAwDJ5x6v12pr9+mDzETm+D08DO7TUbwd3UC8WyQUAuClCFACgyRWUVWv2mgNasPmwar+/bq9ffIRuG9xBfdqFW1wdAABnRogCADSZovIavZ55QO9tylXN9+Gpb/tw3Takg/q2j7C2OAAAzhEhCgBgumOVtZqz7qDeXn9IVQ6nJCmlXZhuG5yg9PgIa4sDAMBFhCgAgGnKaxz677eHNHfdQZXX1EmSktuE6neDE9S/Q4RsNpvFFQIA4DpCFADgvKuqrdO7G3P1euYBHatySJKSokL0uyEJGtqpFeEJAODRCFEAgPOmts6p9zcf0aur96uwvEaSFN/SrtsGd9AlXaPkQ3gCAHgBQhQA4GdzOA0t3panl7/Zp8Ol1ZKkNmGBunVQB43pESM/H8ITAMB7EKIAAD+Z0zD02Y4C/fvrfdp3tFKSFBkSoFsGxuuqXrHy9/WxuEIAAM4/QhQAwGWGYWjl7mL9v1V7lV1QLkkKD/LTr/vH6ZqUtgry97W4QgAAzGNJiCoqKtL999+vzMxM+fr6auzYsZo5c6b8/E4u580339Ts2bOVn5+v6Oho/epXv9INN9zQsP2ll17SnDlzVFpaql69eunBBx9Up06dmvLtAECzYRiGMveX6P+t2qsth49LkkICfHVDenv9sm87tQjkd3MAAO9nyf/tpk2bppiYGK1cuVKFhYWaMmWKZs+erVtvvbXRfp999pmeeuopvfTSS+rTp482btyo3/72t4qMjNSoUaO0YMECzZkzR6+88ori4+P19NNPa+rUqVq0aBEzPwHAebbp0DG9sGqvvj1wTJIU5Oej6/q2003p7RVu97e4OgAAmk6TX6y+b98+ZWZmasaMGbLb7YqLi1NGRobmzp170r55eXmaPHmyUlJSZLPZlJqaqgEDBmjt2rWSpHfeeUfXX3+9kpKSFBgYqOnTpys3N1dr1qxp6rcFAF5rV2G5pr+/Vbe+tUnfHjgmf1+brkttqwW39tfvh3YkQAEAmp0mPxOVnZ2tiIgIxcTENIwlJiYqNzdXpaWlCgsLaxj/8WV7Uv1lgGvXrtW9994rScrJydHkyZMbtvv7+yshIUFZWVkaOHDgOdfkLietTtThLvV4G/prPnpsrqbu7+FjVXrx6336aGueDEk+NunK5FhNHhSv2LCgpimiiXEMm4v+mo8em4v+ms/KHrvymk0eosrLy2W32xuNnXhcUVHRKET9WEFBgW677TYlJyfriiuuOO1zBQUFqaKiwqWaWrcOdWl/s7lbPd6G/pqPHpvL7P4Wl9fouWU5mvPNPtXUOSVJl/WM1d2juqpzdAtTX9tdcAybi/6ajx6bi/6az9177FKIWr16tQICAk65zTAMHT9+XMOGDTvjcwQHB6uysrLR2InHISEhp/yZjRs36s4771R6eroef/zxhgko7Ha7qqqqGu1bVVV12uc5naKi4zIMl37EFDZb/QHjLvV4G/prPnpsLrP7W1FTp/9+e1Bz1h5UeU2dJCk9Lly/v7CjktuESTJUWHj8/L+wG+EYNhf9NR89Nhf9NZ+VPT7x2ufCpRD19ddf66677jrt9qeeeuqsISopKUklJSUqLCxUZGSkJGnXrl2KjY1VaOjJRc+bN0+PPPKIpk6dqltuueWk58rOztbw4cMlSbW1tdq7d6+6dOniytuSYcit/iG4Wz3ehv6ajx6b63z3t7bOqQXfHdErq/epuKJWktQ1uoV+PzRBAzq0lM1ma3b/PTmGzUV/zUePzUV/zefuPXYpRBlneSdn2y5JCQkJSktL02OPPaaHHnpIR48e1fPPP6+JEyeetO+SJUv0wAMP6IUXXtDQoUNP2j5hwgQ988wzuvDCC9WxY0c9/fTTioyMVHp6+rm/KQBoppyGoaVZBfp/q/bq0LH6s/rtI4I0ZUiCLukaJR8u+gcA4JRcClFnmzb8XKcVnzVrlh566CGNGDFCPj4+uuqqq5SRkSFJSk1N1YMPPqixY8fq2WefVV1dnaZOndro56+88ko99NBDmjhxoo4fP67bb79dxcXF6tWrl1588UX5+zNTFACcjmEY+nrvUT23ck/DQrmtgv01eVAHXdUrVn6+TT5xKwAAHsWSdaIiIyM1a9asU27bsGFDw98XLVp0xuex2Wy65ZZbTrrMDwBwaptzS/Xsyj1af7B+raeQAF/9un+cftG3nez+vhZXBwCAZ2BpeQBoBvYUVej5r/ZoeU6RJCnA16ZrUtrp5gFximCdJwAAXHJe74kCALiXI6VVeumbffpwa56cRv1aT1f0jNHkQR28dq0nAADM5lKIat++vdavXy/DME66/8kwDLVq1eq8FgcA+GlKKms1e80BvbvxkGrq6n8BNqxza025IEGdWru2DAQAAGjMpRB13XXXnXF7WlrazyoGAPDzVNXW6e0NuZqduV9l1fVrPfVtH67fD+2oXm1PvZg5AABwDfdEAYAXqHMa+nh7nl74aq/yy2okSUlRIbp9aEcNTmh5zrOnAgCAsyNEAYCH+2ZvsZ758ofpymNDAzXlggRd1j2atZ4AADCBSyHqgQce0NixYxvdE3Xi74Zh6N1339UTTzxhSqEAgMZ25JfpmS93a82+EklSi0Bf3TIgXtemtlOgH2s9AQBgFpdCVGhoqPr27Xva7cuXL/+59QAAzuLwsSq9sGqvPt6WL0OSv69N16S01W8GxDNdOQAATcClEHW2a+q55h4AzFNaVauXFm/Xa6v2NMy4N6pblKZckKB24XaLqwMAoPngnigAcHM1Dqfe3ZirV9fsV2mVQ5KUFheuqRd2Uo/YUIurAwCg+SFEAYCbchqGPs0q0PNf7VFuabUkqUtMC2UMSWDGPQAALESIAgA3tG5/iWZ9uVvb88okSVEtAvS7IQm6+aLOOlpcJsOwuEAAAJoxl0JUSUmJ1q9ff9rZ+Q4fPmxKkQDQXOQUluu5lXv01e5iSVJIgK9+1S9Ov0xrp+AAX/n6cPYJAACruRSiHnrooTNuT0tL+1nFAEBzlX+8Wv/+ep8WbT0ipyH5+tg0oXcbTRoUr1bBAVaXBwAAfoTL+QDAQuU1Dv0n84DmfntI1Q6nJGlEl0hlXNBR8S2ZcQ8AAHdEiAIACzichhZuOaIXV+1VcUWtJKlP2zBNvaiTercNs7g6AABwJi6FqHfffVeJiYmN7ok6wTAMbdu2TTfddNN5LRAAvM3qvcX654rd2lVYIUmKb2nXHUM76qLOrZlxDwAAD+BSiDpw4ICuueaa025fsWLFzy4IALzV7qJy/WvFbn2956gkKSzIT5MHddCEPm3k7+tjcXUAAOBccTkfAJjsaEWNXvx6n97/7rDqDMnPx6ZrU9vqlgHxCrf7W10eAABwESEKAExS7XDq7fWH9Oqa/SqvqZMkDevcWndc2IlJIwAA8GCEKAA4zwzD0Oc7C/XMyj3KPVYlSeoW3ULThnVSWlyEtcUBAICfjRAFAOfRlsOlenr5bn2XWypJimoRoNsv6KjRPaLlw6QRAAB4BZdClGEYP2s7AHirI6VVenblHi3JKpAkBfn56Ff943RjenvZ/X0trg4AAJxPLoWoQYMGaf369afcZhiGUlJSzkdNAOAxymscej3zgP77/WK5NklX9IzRlAsSFNUi0OryAACACVwKUYMHDzarDgDwKHXfL5b7/360WG5aXLj+76JEdY1pYXF1AADATNwTBQAu+vZAif6xbJeyC8ol1S+WO/XCTrowsRWL5QIA0Ay4FKK2b9+uyspKSY3vf7LZbDIMQzabTX379j2/FQKAm8g9VqVZX+7W5zsLJUmhgX6aPLiDJrJYLgAAzYpLIerjjz/WXXfdddrtTz31FCEKgNepqKnT65n79ca6g6qpM+Rjk67u3Ua/G5ygiGAWywUAoLlhdj4AOA2nYeiT7fl6duUeFZTVSJLS4yM0fViiOkeFWFwdAACwiksh6mzX+nMvAABvseVwqf6xbJe2HD4uSWoXHqRpF3XSRZ1b81kHAEAzx8QSAPAjBWXVenblHi3eli9JCvb31W8GxOmXae0V6Md9TwAAgBAFAJKkaodT//32oF5bs1+VtU5J9es93X5BgiJZ7wkAAPwIIQpAs2YYhpZlF+pfK3Yrt7RaktSrTZimX5yonrGhFlcHAADckUshqkWLFlq/fn3DdOaSGv5uGIb8/MhkADzHrsJy/X3ZLq3bXyJJim4RoDsu7KRR3aK47wkAAJyWS6nnt7/97Rm3p6Wl/axiAKApHK9y6N/f7NO7Gw6pzpACfG26sV+cbu4fJ7u/r9XlAQAAN8epIwDNhtMwtGjLET23cq+OVtZKkoZ1bq1pwzqpXbjd4uoAAICnIEQBaBa2HC7Vk1/s0rYj9VOWJ7Sy6+7hnTUgoaXFlQEAAE/jUoh6+umnddFFF532nqglS5boj3/8oymFAsBPUVReo2dX7tGHW/MkSSEBvrp1UAddl9pW/r5MWQ4AAFznUohyOp3q27fvabcvX77859YDAOeFo86pdzbm6t9f71N5TZ0k6fKeMfr90I6KDAmwuDoAAODJXApRZ5utitmsALiDNfuO6h9f7NKe4gpJUveYFrr74s7q3TbM4soAAIA34J4oAF4j91iV/rlit5ZlF0qSIuz+uv2CBI3tFSsffskDAADOE0IUAI9X7XDqP2sP6PXMA6p2OOVrkyamtNVvB3dQWJC/1eUBAAAvQ4gC4NG+3lOsJ7/I0cGSKklSWly47h7eWZ2jQiyuDAAAeCuXQlRVVZXy8vJOu72srOxnFwQA5+JIaZWeWv7DpXuRIQH6v2GdNLJrFPdnAgAAU7kUoiZNmiSHw3HG7QBgpto6p+auO6hXVu9X1feX7l3Xt50mD+qgFoGcXAcAAOZz6RtHTEyMWXUAwFmt3X9Uf/s8R3uLKyVJqe3C9IcRSVy6BwAAmhS/tgXg9grKqvXP5bu1dEeBJKlVsL+mXthJY3pEc+keAABoci6FqKVLlyoyMlKGYTR8cTnxd8MwdPDgQY0bN86UQgE0P446p97eUL9gbkVtnXxs0sQ+bfW7IQkKDeJ3QAAAwBoufQvZsmWL7rrrrtNuX7Fixc8uCAAkacPBY/rb5znKKSyXJCW3CdXMEZ3VLSbU4soAAEBz51KIMgzjZ20HgLMpKq/RM1/u1kfb8iVJ4UF+uuPCjroymQVzAQCAe3ApRJ3t3gPuTQDwU9U5Db236bBeWLVHZdV1skm6qnesMi7oqAg7C+YCAAD3wU0FACy35XCpnvgsRzvy69ea6x7TQjNHdFbPNmEWVwYAAHAyQhQAy5RW1eq5lXu14LvDMiSFBvop44IEXd27jXx9OLMNAADcE/dEAWhyhmFoaVaBnlq+S8UVtZKky3vGaOqFHdUqOMDi6gAAAM7MpRDVo0cPrV+/vtEU5ycYhqFOnTqd0/MUFRXp/vvvV2Zmpnx9fTV27FjNnDlTfn6nL2fJkiX629/+ps8//7xhzOl0Ki0t7aR6Vq1apeDgYFfeGoAmcrCkUn/9LEer9x2VJCW0suueS5KUFhdhbWEAAADnyKUQNXr06DNuT0tLO6fnmTZtmmJiYrRy5UoVFhZqypQpmj17tm699daT9q2trdXs2bP1z3/+UzExMY225eTkqLa2VuvXr1dAAL+9BtxZbZ1Tb6w7qFdW71e1w6kAX5tuGRivm9LjFODnY3V5AAAA56zJ74nat2+fMjMz9eWXX8putysuLk4ZGRl68sknTxmibrnlFgUGBmry5MlauHBho22bN29W165dCVCAm9tw8Jge/yxbe4oqJEn94yM085Ikxbe0W1wZAACA61wKUcXFxaqtrT3jPv97tuh/ZWdnKyIiotF+iYmJys3NVWlpqcLCGs/G9eSTTyo2Nlbz588/6bk2b96s6upqTZgwQYcOHVJiYqKmT5+uvn37uvCuJHeZmf1EHe5Sj7ehv+b73x4fq6zVrC/36IPNRyRJrYL99X/DOumy7tEsifATcAybjx6bi/6ajx6bi/6az8oeu/KaLoWoF154Qb/5zW9OmkDCZrPJMAy9/PLL+stf/nLG5ygvL5fd3vi3zyceV1RUnBSiYmNjT/tcQUFB6t27t+68806Fh4dr7ty5mjRpkhYuXKi4uLhzfl+tW4ee875Nwd3q8Tb013ytWrXQgg2H9OhH21VUXiNJ+mX/OM28rJsimDjiZ+MYNh89Nhf9NR89Nhf9NZ+799ilEBUUFKS2bduednuLFi3O+hzBwcGqrKxsNHbicUhIiCvl6J577mn0eNKkSZo/f75WrFihG2+88Zyfp6jouNxhYkGbrf6AcZd6vA39NZ/NJh1z2jTz3U1au79EktSpdbD+eGmSUtqFy1FRrcKKamuL9GAcw+ajx+aiv+ajx+aiv+azsscnXvtcuBSiznb5zblcnpOUlKSSkhIVFhYqMjJSkrRr1y7FxsYqNNS1xPn0009r1KhR6tGjR8NYTU2NAgMDXXoew5Bb/UNwt3q8Df01R43Dqf+sPaDXMg+oxuFUoJ+Pbh0YrxvS28vf14een0ccw+ajx+aiv+ajx+aiv+Zz9x43+ZRYCQkJSktL02OPPaaysjIdOHBAzz//vCZOnOjyc+3cuVOPPvqoCgoKVFNTo2effVZlZWUaOXKkCZUDOJ1Nh47pxjnr9eLX+1TjcGpQQku99es03TwgXv6+zLwHAAC8iyXfbmbNmiWHw6ERI0bo2muv1dChQ5WRkSFJSk1NPWkWvtN5/PHHFR8fr3HjxmnAgAHKzMzUa6+9poiICBOrB3BCWbVDT3yWrVvf2qQ9xRVqHeyvWb9M1awJyWofwcx7AADAOzX5FOeSFBkZqVmzZp1y24YNG045Pn78eI0fP77RWEREhB5//PHzXh+As1uWXagnv8hRQVn9xBHjkmN157CO6tS+lQoLuVYcAAB4L5dClNPp1Pr162UYRsP9Tyf+bhiGKioqTCkSgPsoKKvW3z7P0fKcIklSfEu7/jgySWlxEUz5CgAAmgWXQtTdd999xu1paWk/qxgA7stpGFrw3WE98+UeldfUydfHpl/1a69bBsQryN/X6vIAAACajCWX8wHwLHuLKvTopzu18VCpJKlHbKjuuzRJSVFnX9YAAADA27gUol555RWlpqaedrHdb775Rr///e/Pa4EArFNb59TrmQf06pr9qq0zZPf30ZQLOuralLby9eHaPQAA0Dy5FKJKSkrUt2/f025fvnz5z60HgJvYnndcDy/ZqeyCcknSkI6tNPOSzmoTFmRxZQAAANZq8sV2Abi3aodTL3+zT3PWHlCdIUXY/XX38ERd2i2Kf+MAAADinigAP7Lp0DE9snSn9hZXSpJGdo3SjIsT1TI4wOLKAAAA3AchCoAqa+v0wld79db6QzIktQ4J0D0jOmtYUqTVpQEAALgdQhTQzK3bX6JHlu7UoWNVkqQresbo/4Z1UliQv8WVAQAAuCeXQtT/zsrn6nYA7qOs2qFnV+7Re5sOS5JiQgP1x5FJGtyxlcWVAQAAuDeXQtSoUaO0fv36U24zDEMXXXTReSkKgLlW7SnW459mK+94tSRpQp82+v3QjmoRyMlpAACAs3HpG1NycrJZdQBoAscqa/X0it36aGueJKldeJDuH9VFaXER1hYGAADgQfi1M9BMLM8u1BOf56iovEY2Sb9Ma6ffDUmQ3d/X6tIAAAA8CiEK8HKlVbV68otd+mR7viQpoZVd94/qqt5twyyuDAAAwDMRogAv9vWeYj2ydKcKymrkY5Nu6henyYM6KNDPx+rSAAAAPBYhCvBC5TUO/WvFbi347ogkKb6lXQ+O7qrkNpx9AgAA+LkIUYCX+fZAiR76ZIdyS+tn3vtl33bKuCBBQdz7BAAAcF4QogAvUVVbp+e+2qu31h+SJLUNC9SfL+vKzHsAAADnGSEK8AJbDpfqgY93aN/RSknS1b1jdedFnRQSwD9xAACA841vWIAHq3E49fLqfXo984CchhTVIkD3XdpFgzu2sro0AAAAr0WIAjzUzvwyPfDJDmUXlEuSRneP1t0XJyosyN/iygAAALwbIQrwMA6nof9kHtBL3+yTw2mopd1f94xM0sVJkVaXBgAA0CwQogAPsq+4Qn/5eIe2HjkuSRrWubXuHZmkVsEBFlcGAADQfBCiAA9gGIbe33xETy3bpSqHUy0CfTXj4s4a3T1aNpvN6vIAAACaFUIU4OZKKmr16Kc7tTynSJKUHh+hBy7rqpjQQIsrAwAAaJ4IUYAbW7P3qB74ZIcKy2vk52PT7UM76vq0dvLh7BMAAIBlCFGAG6p2OPX8V3v032/rF87t2CpYD4/ppq4xLSyuDAAAAIQowM3sKizX/YuzGqYun9inje68qJOC/H0trgwAAAASIQpwG4Zh6J0NuZr15W7V1NVPXX7/qC4amtja6tIAAADwI4QowA0Ultfo4SU79PWeo5KkwR1b6s+juqp1CFOXAwAAuBtCFGCxb/YW64GPd6i4olYBvjbdeVEnXZPSlqnLAQAA3BQhCrBIbZ1TL3y1V3PWHZQkdY4M0SOXd1NiZIjFlQEAAOBMCFGABQ6WVOq+j7K09chxSUweAQAA4EkIUUATW5qVr8c+zVZ5TZ1CA/10/6guGp4UaXVZAAAAOEeEKKCJVNXW6e/LdumDzUckSb3bhumRy7upTViQxZUBAADAFYQooAnkFJTrjx9t156iCtkk/WZAnCYPTpCfD5NHAAAAeBpCFGAiwzC04LvDemr5blU7nGodEqCHx3RVv/iWVpcGAACAn4gQBZjkeJVDj366U5/vLJRUv/bTXy7rqlbBrP0EAADgyQhRgAm25x3XPYu2K/dYlXx9bPr90I66Pq2dfFj7CQAAwOMRooDzyDAMzdt0WE8v36XaOkNtw4P02BXd1TM21OrSAAAAcJ4QooDzpLzGoUeXZuvTHQWSpGGdW+vPo7oqNIh/ZgAAAN6Eb3fAeZBdUKZ7Fm3X/qOV8vWxaeqFHfXLvu1k4/I9AAAAr0OIAn4GwzC0cMsRPfnFLlU7nIpuEaDHr+yh3m3DrC4NAAAAJiFEAT9RZW2d/vpZtj7ali+pfva9By/rpohgf4srAwAAgJkIUcBPsKeoQjMXbdOeogr52KTfDUnQr/vHMfseAABAM0CIAlz08fY8PbY0W1UOpyJDAvTI5d2UFhdhdVkAAABoIoQo4BzV1jn19PLdendjriSpX3yEHh7TTa1DWDwXAACgOSFEAeegsKxa9yzark25pZKkSQPjNXlQB/n6cPkeAABAc0OIAs5i06FjumfRdhWW1ygkwFcPj+mmoYmtrS4LAAAAFiFEAadhGIbe23RY/1i2Sw6noU6tg/XkuJ6Kb2m3ujQAAABYiBAFnEK1w6knPsvWh1vzJEmXdInU/aO6KjjA1+LKAAAAYDVCFPA/jpRW6Q8Lt2l7Xpl8bNLvh3bUjentZWP6cgAAAIgQBTSydv9R/fHDLJVU1io8yE+PXtFdAzq0tLosAAAAuBFCFKD6+5/eWHdQz67cI6chdYtuob+N66E2YUFWlwYAAAA342PFixYVFSkjI0Pp6ekaMGCAHn30UTkcjjP+zJIlSzRixIiTxl966SVdeOGFSklJ0U033aTdu3ebVTa8VEVNnf74YZZmfVkfoC7vGaOXftGHAAUAAIBTsiRETZs2TcHBwVq5cqXmzZunb775RrNnzz7lvrW1tXrppZd01113yTCMRtsWLFigOXPm6JVXXtGaNWvUs2dPTZ069aT9gNM5cLRSt7y5QZ/tLJCvj01/GNFZfxnVRUH+TCABAACAU2vyELVv3z5lZmZqxowZstvtiouLU0ZGhubOnXvK/W+55RatWbNGkydPPmnbO++8o+uvv15JSUkKDAzU9OnTlZubqzVr1pj9NuAFvtpdpF/NXa9dhRVqHRKgF6/trWtS2jKBBAAAAM6oye+Jys7OVkREhGJiYhrGEhMTlZubq9LSUoWFhTXa/8knn1RsbKzmz59/0nPl5OQ0Clf+/v5KSEhQVlaWBg4ceM41uct35hN1uEs93uaHvhp6dc0B/b+v9sqQ1LttmP46truiWgRaWJ134Bg2F/01Hz02F/01Hz02F/01n5U9duU1mzxElZeXy25vvFjpiccVFRUnhajY2FiXnisoKEgVFRUu1dS6dahL+5vN3erxJpU1dXpgaY4+/O6wJOnGgfH68xU9FeBnyZWtXotj2Fz013z02Fz013z02Fz013zu3uMmD1HBwcGqrKxsNHbicUhIiEvPZbfbVVVV1WisqqrK5ecpKjoud7iNymarP2DcpR5vk3e8SjM/zNKWQ6Xy9bFp5ojOGt+njUpLyq0uzWtwDJuL/pqPHpuL/pqPHpuL/prPyh6feO1z0eQhKikpSSUlJSosLFRkZKQkadeuXYqNjVVoqGuJMykpSdnZ2Ro+fLik+kko9u7dqy5durj0PIYht/qH4G71eINNh47pDwu3qbiiVhF2f/11bHf1bR9Bn03CMWwu+ms+emwu+ms+emwu+ms+d+9xk1/DlJCQoLS0ND322GMqKyvTgQMH9Pzzz2vixIkuP9eECRP0xhtvKCsrS9XV1frHP/6hyMhIpaenm1A5PNXCzUf0u3e+U3FFrbq3CdOcG1PVt32E1WUBAADAQ1lyI8isWbPkcDg0YsQIXXvttRo6dKgyMjIkSampqVq4cOE5Pc/EiRN188036/bbb9fAgQO1bds2vfjii/L39zezfHgIh9PQP5bt0sNLd8rhNHRxUqTemzJIbcJZ/wkAAAA/nc1gUSUVFrrHda02mxQZGeo29XiysmqH7v1wu1bvPSpJ+u3gDrp1ULyio8Lor4k4hs1Ff81Hj81Ff81Hj81Ff81nZY9PvPa5aPJ7ogCzHS6t0v8t2KJdhRUK8vPRg2O66eKkSKYjBQAAwHlBiIJX2Xq4VHe9v1XFFbWKDAnQU1f3VPcY954iEwAAAJ6FEAWv8fnOAv3l4x2qdjiVFBWip67qqdgw7n8CAADA+UWIgsczDEP/WXtQz67cI0ka0rGVHr2im0ICOLwBAABw/vEtEx6tts6pv36Wow+2HJEkXZfaVtOGJcrPhxugAAAAYA5CFDxWaVWtZi7arnX7S+Rjk+4alqjr+razuiwAAAB4OUIUPNLBkkpNm79F+45WKtjfV49e0U0XdGptdVkAAABoBghR8DibDh3T3R9sU0llraJbBOjpq5PVJbqF1WUBAACgmSBEwaN8sj1fDy3Zodo6Q91jWuipq3oqskWg1WUBAACgGSFEwSMYhqGXV+/Xv7/eJ0ka1rm1HhrTTXZ/X4srAwAAQHNDiILbq3E49cjSnfp4e74k6cb09rrjwo7ysTEDHwAAAJoeIQpurbSqVjM+2Kb1B4/J1yb94ZIkje/dxuqyAAAA0IwRouC2jpRWaer8LdpTVKGQAF/99coeGpDQ0uqyAAAA0MwRouCWduSXadr8LSosr1F0iwD9c3yykqKYgQ8AAADWI0TB7azZe1QzF21TeU2dOrUO1r/GJys2LMjqsgAAAABJhCi4mY+25unhpTtV5zSUHheuv43tqdAgDlMAAAC4D76dwi0YhqFX1+zX/1tVP4X5qG5R+vOorgrw87G4MgAAAKAxQhQsV+c09OQXOXpv02FJ0q/7xynjggSmMAcAAIBbIkTBUjUOp/78cZY+31kom6S7L+6sa1PbWl0WAAAAcFqEKFimvMahGR9s09r9JfL3tenhMd00okuU1WUBAAAAZ0SIgiVKKmo1df5mbc8rk93fR0+O66kBHVgDCgAAAO6PEIUmd6S0Sr+ft1n7jlYqwu6vf45PVs/YUKvLAgAAAM4JIQpNak9RhX4/7zvll9UoJjRQz07opYTWwVaXBQAAAJwzQhSazNbDpbpz/hYdq3KoY6tgzZrAIroAAADwPIQoNIlvD5TorgVbVVFbp56xofrn+GRF2P2tLgsAAABwGSEKpvtmb7FmfLBN1Q6n0uMj9I9xPRUc4Gt1WQAAAMBPQoiCqVbkFOreD7erts7QBZ1a6YkreyjQz8fqsgAAAICfjBAF0yzZnq+/fJylOkMa0SVSD4/pJn9fAhQAAAA8GyEKpli4+YgeWbpThqQxPaJ1/6iu8vOxWV0WAAAA8LMRonDevbPhkJ78YpckaUKfNvrDiM7ysRGgAAAA4B0IUTiv5qw9oFlf7pEkXZ/WTtMu6iQbAQoAAABehBCF8+bHAWrSwHjdNrgDAQoAAABehxCF8+K/3x5sCFC3De6gWwd1sLgiAAAAwBxMlYaf7Z0Nh/T08t2SpFsHxhOgAAAA4NUIUfhZ5m3MbZhE4jcD4vTbwQQoAAAAeDdCFH6yBd8d1l8/z5Ek/apfe00ZksA9UAAAAPB6hCj8JAs3H9Fjn2ZLqp+F7/dDOxKgAAAA0CwQouCyxdvy9MjSnZKk61LbMo05AAAAmhVCFFyyIqdID36yQ4bqF9KdPjyRAAUAAIBmhRCFc7bh4DH96aPtchrSFT1j9IcRnQlQAAAAaHYIUTgn2QVluuv9Lap2OHVhYmv96dIu8iFAAQAAoBkiROGsco9V6Y73tqisuk4p7cL06OXd5OdDgAIAAEDzRIjCGR2vcmja/C0qKq9R58gQPXVVsoL8fa0uCwAAALAMIQqn5ahzauaibdpTXKHoFgH61/hkhQb5WV0WAAAAYClCFE7JMAw98XmO1u4vkd3fR09dnazo0ECrywIAAAAsR4jCKb2x7qA+2HxEPjbp0cu7q2t0C6tLAgAAANwCIQon+XpPsZ75co8k6f+GJWpoYmuLKwIAAADcByEKjRwsqdT9i7NkSLq6d6x+0bed1SUBAAAAboUQhQZVtXX6w8JtKq1yKLlNqO4e3tnqkgAAAAC3Q4iCpPqJJB7/LFvZBeVqFeyvJ67soQA/Dg8AAADgf/EtGZKkdzfmavG2fPnapMeu6K4YZuIDAAAATokQBW06dExPLd8tSZp6USelxUVYWxAAAADgxghRzVxhWbVmLtquOqehkV2j9EsmkgAAAADOiBDVjNXWOXXPou0qKq9RYmSw7h/VRTabzeqyAAAAALdmSYgqKipSRkaG0tPTNWDAAD366KNyOByn3HfFihW68sorlZKSotGjR2vZsmUN25xOp1JTU5WSkqLU1NSGPxUVFU31Vjzav1bs1qbcUrUI9NXfxvaU3d/X6pIAAAAAt+dnxYtOmzZNMTExWrlypQoLCzVlyhTNnj1bt956a6P99u7dqzvuuENPPfWUhg0bpqVLl2ratGlaunSpYmJilJOTo9raWq1fv14BAQFWvBWPtTy7UG9vyJUkPTS6m+Jb2i2uCAAAAPAMTX4mat++fcrMzNSMGTNkt9sVFxenjIwMzZ0796R9FyxYoPT0dF1yySXy8/PTmDFj1K9fP7399tuSpM2bN6tr164EKBcVltfo0U+zJUk3pbfX0MTWFlcEAAAAeI4mPxOVnZ2tiIgIxcTENIwlJiYqNzdXpaWlCgsLaxjPyclRly5dGv18586dlZWVJak+RFVXV2vChAk6dOiQEhMTNX36dPXt29elmtzlNqATdZhZj2EYenjJDpVU1qpLVIimXJDgNu/fbE3R3+aOHpuL/pqPHpuL/pqPHpuL/prPyh678ppNHqLKy8tltze+dOzE44qKikYh6lT7BgUFNdzzFBQUpN69e+vOO+9UeHi45s6dq0mTJmnhwoWKi4s755patw79qW/HFGbWM2f1Pn2956gC/Hz07I1pahvjXu+9Kbjbf29vRI/NRX/NR4/NRX/NR4/NRX/N5+49bvIQFRwcrMrKykZjJx6HhIQ0Grfb7aqqqmo0VlVV1bDfPffc02jbpEmTNH/+fK1YsUI33njjOddUVHRchnHOu5vGZqs/YMyqZ29xhR75cJsk6Y6hHdXKVyosPH7+X8hNmd1f0GOz0V/z0WNz0V/z0WNz0V/zWdnjE699Lpo8RCUlJamkpESFhYWKjIyUJO3atUuxsbEKDW1cdJcuXbR169ZGYzk5OUpOTpYkPf300xo1apR69OjRsL2mpkaBgYEu1WQYcqt/CGbU46hz6v6PslTtcKp/fISuTW3rVu+5Kbnbf29vRI/NRX/NR4/NRX/NR4/NRX/N5+49bvKJJRISEpSWlqbHHntMZWVlOnDggJ5//nlNnDjxpH3Hjh2rzMxMLV68WA6HQ4sXL1ZmZqbGjRsnSdq5c6ceffRRFRQUqKamRs8++6zKyso0cuTIpn5bbu+lb/Zpe16ZwoL89JfLusqHi3kBAACAn8SSdaJmzZolh8OhESNG6Nprr9XQoUOVkZEhSUpNTdXChQsl1U848dxzz+nFF19Uv3799Pzzz+uZZ55Rx44dJUmPP/644uPjNW7cOA0YMECZmZl67bXXFBERYcXbclubDh3T7MwDkqR7L0lSdKhrZ+oAAAAA/MBmGO58oqxpFBa6x3WtNpsUGRl6Xuspq3bohjnrlXusSpf3iNYDo7udnyf2QGb0F43RY3PRX/PRY3PRX/PRY3PRX/NZ2eMTr30uLDkThabz1LJdyj1WpTZhgbr74s5WlwMAAAB4PEKUF1uRU6RFW/Nkk/Tg6G5qEdjk84gAAAAAXocQ5aWOVdbq8c+yJUk3pLdXavtwiysCAAAAvAMhyks9vXyXispr1KGlXbcN7mB1OQAAAIDXIER5oa92F+mjbfmySfrzZV0V5O9rdUkAAACA1yBEeZnjVQ499mn9ZXzXp7VX77ZhFlcEAAAAeBdClJf514rdKiirUXxLu343hMv4AAAAgPONEOVFNh48pg+2HJEk3XdpFy7jAwAAAExAiPISjjpnw2x843rFMhsfAAAAYBJClJf477eHtLuoQhF2f/1+aEerywEAAAC8FiHKC+Qeq9K/v9knSbrzoo6KsPtbXBEAAADgvQhRXuDvX+So2uFU3/bhurxHjNXlAAAAAF6NEOXhVu8t1srdxfLzsemeS5Jks9msLgkAAADwaoQoD2YYhl76Zr8kaWJKW3VsHWxxRQAAAID3I0R5sHUHSvRdbqkCfG36Vb/2VpcDAAAANAuEKA/2yur6s1DjerVRVItAi6sBAAAAmgdClIfacPCYvj1wTH4+nIUCAAAAmhIhykO9srp+SvMrk2MUGxZkcTUAAABA80GI8kCbc0u1Zl+JfH1surl/vNXlAAAAAM0KIcoDvbqm/l6oMd2j1Tacs1AAAABAUyJEeZh9xRX6anexbJJ+3T/O6nIAAACAZocQ5WHe3pArSbqgUyt1aMW6UAAAAEBTI0R5kNKqWi3ackSS9Mu0dhZXAwAAADRPhCgP8sHmI6pyONU5MkTpcRFWlwMAAAA0S4QoD+FwGnrn+0v5ftm3nWw2m8UVAQAAAM0TIcpDrMgp1JHj1Yqw+2tU92irywEAAACaLUKUh3jz20OSpAl92ijQj/9sAAAAgFX4Nu4Btucd16bcUvn52DSxTxurywEAAACaNUKUB/hgc/2MfCO6RCqyRaDF1QAAAADNGyHKzVXV1mlJVr4kaWxyrMXVAAAAACBEubkVOUUqq65TbGig0uMjrC4HAAAAaPYIUW7uw615kqTLe8bIh2nNAQAAAMsRotzYkdIqrdl3VJJ0Rc8Yi6sBAAAAIBGi3NribfkyJPVtH672EXarywEAAAAgQpTbMgxDH26tn5XvymTOQgEAAADughDlpjYdKtWBkirZ/X10cVKU1eUAAAAA+B4hyk0t+v4s1CVdohQc4GtxNQAAAABOIES5IYfT0BfZhZKkK7iUDwAAAHArhCg3tDm3VGXVdQoP8lOftuFWlwMAAADgRwhRbujrPcWSpIEJLeXrw9pQAAAAgDshRLmhVbvrQ9Tgjq0srgQAAADA/yJEuZkjx6q0s6BcNkmDElpaXQ4AAACA/0GIcjMrduZLknrEhqplcIDF1QAAAAD4X4QoN7N8R4EkaXBHzkIBAAAA7ogQ5UYcdU599f3U5kO4HwoAAABwS4QoN7Ipt1THqx2KsPure2yo1eUAAAAAOAVClBv5es9RSdKgji3lY2NqcwAAAMAdEaLcyImpzbmUDwAAAHBfhCg3kXe8WjmF5bLZpIEdmFQCAAAAcFeEKDex5XCpJCklLkIRwf4WVwMAAADgdPysLgD1ercN06XdovSboYlWlwIAAADgDAhRbiKqRaAeu6K7IiNDVVh43OpyAAAAAJwGl/MBAAAAgAsIUQAAAADgAktCVFFRkTIyMpSenq4BAwbo0UcflcPhOOW+K1as0JVXXqmUlBSNHj1ay5Yta7T9pZde0oUXXqiUlBTddNNN2r17d1O8BQAAAADNlCUhatq0aQoODtbKlSs1b948ffPNN5o9e/ZJ++3du1d33HGH7rzzTq1bt0533HGHpk2bpry8PEnSggULNGfOHL3yyitas2aNevbsqalTp8owjCZ+RwAAAACaiyYPUfv27VNmZqZmzJghu92uuLg4ZWRkaO7cuSftu2DBAqWnp+uSSy6Rn5+fxowZo379+untt9+WJL3zzju6/vrrlZSUpMDAQE2fPl25ublas2ZNU78tAAAAAM1Ek8/Ol52drYiICMXExDSMJSYmKjc3V6WlpQoLC2sYz8nJUZcuXRr9fOfOnZWVldWwffLkyQ3b/P39lZCQoKysLA0cOPCca7LZfuq7Ob9O1OEu9Xgb+ms+emwu+ms+emwu+ms+emwu+ms+K3vsyms2eYgqLy+X3W5vNHbicUVFRaMQdap9g4KCVFFRcU7bz1Xr1qEu7W82d6vH29Bf89Fjc9Ff89Fjc9Ff89Fjc9Ff87l7j5s8RAUHB6uysrLR2InHISEhjcbtdruqqqoajVVVVTXsd7bt56qo6Ljc4TYqm63+gHGXerwN/TUfPTYX/TUfPTYX/TUfPTYX/TWflT0+8drnoslDVFJSkkpKSlRYWKjIyEhJ0q5duxQbG6vQ0MZFd+nSRVu3bm00lpOTo+Tk5Ibnys7O1vDhwyVJtbW12rt370mXAJ6NYcit/iG4Wz3ehv6ajx6bi/6ajx6bi/6ajx6bi/6az9173OQTSyQkJCgtLU2PPfaYysrKdODAAT3//POaOHHiSfuOHTtWmZmZWrx4sRwOhxYvXqzMzEyNGzdOkjRhwgS98cYbysrKUnV1tf7xj38oMjJS6enpTf22AAAAADQTlkxxPmvWLDkcDo0YMULXXnuthg4dqoyMDElSamqqFi5cKKl+wonnnntOL774ovr166fnn39ezzzzjDp27ChJmjhxom6++WbdfvvtGjhwoLZt26YXX3xR/v7+VrwtAAAAAM2AzWBRJRUWusd1rTabFBkZ6jb1eBv6az56bC76az56bC76az56bC76az4re3zitc+FJWeiAAAAAMBTEaIAAAAAwAWEKAAAAABwASEKAAAAAFxAiAIAAAAAFxCiAAAAAMAFflYX4A5sNqsrqHeiDnepx9vQX/PRY3PRX/PRY3PRX/PRY3PRX/NZ2WNXXpN1ogAAAADABVzOBwAAAAAuIEQBAAAAgAsIUQAAAADgAkIUAAAAALiAEAUAAAAALiBEAQAAAIALCFEAAAAA4AJCFAAAAAC4gBAFAAAAAC4gRLmBoqIiZWRkKD09XQMGDNCjjz4qh8NhdVkeLSsrS7/5zW/Uv39/DRkyRH/4wx9UXFwsSfrLX/6i5ORkpaamNvx5++23La7YsyxevFg9evRo1MMZM2ZIkjZt2qRrrrlGqampuvjii/Xuu+9aXK3nWbhwYaPepqamKjk5WcnJyZI4hn+u4uJijRw5UmvWrGkYO9txu2DBAo0cOVIpKSkaP368NmzY0NRle4xT9XfJkiUaN26c+vbtq4svvljPPvusnE5nw/bRo0erT58+jY7pXbt2WVG+RzhVj8/2ucAxfO7+t79//vOfT/pM7t69uyZNmtTwMxzD5+ZM38887nPYgOVuvPFGY/r06UZFRYWxf/9+4/LLLzdeeuklq8vyWJWVlcaQIUOMf/3rX0Z1dbVRXFxsTJ482bjtttsMwzCMq6++2pg/f77FVXq2J554wrjnnntOGi8pKTH69+9vvPHGG0Ztba3x9ddfG6mpqcamTZssqNJ7HDlyxBgyZIjx/vvvG4bBMfxzrFu3zrjkkkuMLl26GKtXrzYM4+zH7erVq43U1FRj3bp1Rk1NjfHaa68ZAwYMMCoqKqx8K27pVP3dvHmz0bt3b+OLL74w6urqjJycHGP48OHGK6+8YhiGYRw/ftzo2rWrcfDgQStL9xin6rFhnPlzgWP43J2uvz+2cuVKo3///sbOnTsNw+AYPldn+n7miZ/DnImy2L59+5SZmakZM2bIbrcrLi5OGRkZmjt3rtWleazc3Fx169ZNt99+uwICAtSyZUtdd911Wrt2rWpqarRz586G3+jjp9m8efMpe7h06VJFRETohhtukJ+fnwYNGqQrr7yS4/lnMAxDM2bM0LBhwzRu3DiO4Z9hwYIFuvvuu/V///d/jcbPdty+++67uvzyy5WWliZ/f3/dfPPNatmypRYvXmzF23Bbp+vvoUOH9Itf/ELDhw+Xj4+PEhMTNXLkSK1du1aStGXLFkVERKhdu3ZWlO1RTtfjs30ucAyfm9P198eKi4t19913609/+pOSkpIkcQyfqzN9P/PEz2FClMWys7MVERGhmJiYhrHExETl5uaqtLTUwso8V6dOnfTyyy/L19e3YWzJkiXq2bOnsrKy5HA4NGvWLA0ePFijRo3Sv//970aXleDMnE6ntm7dquXLl2v48OG68MILdf/99+vYsWPKzs5Wly5dGu3fuXNnZWVlWVSt5/vggw+Uk5Oje+65R5I4hn+GCy64QJ9++qnGjBnTaPxsx21OTg7H9Tk4XX9HjRqle++9t+FxVVWVli9frp49e0qq/6WM3W7XjTfeqAEDBmj8+PFatmxZk9buKU7X47N9LnAMn5vT9ffH/v73vys5OVljx45tGOMYPjdn+n7miZ/DhCiLlZeXy263Nxo78biiosKKkryKYRh6+umntWzZMv3pT3/S8ePH1b9/f910001asWKFnnzySc2ZM0evvvqq1aV6jOLiYvXo0UOjRo3S4sWL9dZbb2nv3r2aMWPGKY/noKAgjuWfyOl06oUXXtDvfvc7tWjRQpI4hn+GqKgo+fn5nTR+tuOW4/rcnK6/P1ZWVqbbb79dQUFBuvnmmyVJNptNvXr10iOPPKKVK1fq5ptv1h133KGNGzeaX7SHOV2Pz/a5wDF8bs52DB84cEALFy7U9OnTG41zDLvuf7+feeLn8Jk/7WC64OBgVVZWNho78TgkJMSKkrxGWVmZ7r33Xm3dulVvvPGGunbtqq5du2rIkCEN+/Tu3Vu//vWvtXjxYt16660WVus5IiMjG12eZ7fbNWPGDF177bUaP368qqqqGu1fVVXFsfwTrVmzRvn5+Zo4cWLD2JAhQziGzzO73a7jx483GvvxcWu32095XLds2bLJavQGu3fv1tSpU9W6dWv95z//afjFwP8et2PHjtWHH36oJUuWKCUlxYJKPc/ZPhc4hs+P9957r2FSiR/jGHbNqb6feeLnMGeiLJaUlKSSkhIVFhY2jO3atUuxsbEKDQ21sDLPtn//fk2YMEFlZWWaN2+eunbtKkn67LPP9NZbbzXat6amRkFBQVaU6ZGysrL097//XYZhNIzV1NTIx8dHvXv3VnZ2dqP9c3JyGq4bh2uWLFmikSNHKjg4uGGMY/j869KlyxmP26SkJI7rn2nFihW65pprNHToUL3yyisKDw9v2PbKK6/om2++abR/TU2NAgMDm7pMj3W2zwWO4fNj6dKlGjdu3EnjHMPn7nTfzzzxc5gQZbGEhASlpaXpscceU1lZmQ4cOKDnn3++0W+e4Zpjx47p17/+tfr27atXXnlFrVq1athmGIYef/xxffPNNzIMQxs2bNB//vMfXXfddRZW7FkiIiI0d+5cvfzyy3I4HMrNzdWTTz6pq6++WqNGjVJhYaFmz56t2tparV69WosWLdKECROsLtsjffvtt+rXr1+jMY7h82/kyJFnPG4nTpyoRYsWafXq1aqtrdXs2bNVVFSkkSNHWly5Z9i4caNuv/123XvvvZo5c+ZJl0sdPnxYDz74oA4cOCCHw6F58+Zpw4YNuvrqqy2q2POc7XOBY/jnO3r0qHbt2nXSZ7LEMXyuzvT9zCM/h62aFhA/KCgoMO644w6jf//+xsCBA40nnnjCcDgcVpflsV599VWjS5cuRp8+fYyUlJRGfwzDMN58803j0ksvNfr06WOMGDHCeOONNyyu2POsWbPGuO6664zU1FRj4MCBxsMPP2xUVVUZhmEY3333XcO2ESNGGO+9957F1XqulJQUY/ny5SeNcwz/fP87ffHZjtv333/fGDVqlJGSkmJMnDjR2LhxY1OX7FF+3N/bbrvN6Nq160mfx5MmTTIMwzCqq6uNRx991LjggguMPn36GBMmTDjt1NL4wf8ew2f7XOAYds2pPiO6dOliVFZWnrQvx/C5Odv3M0/7HLYZxo+uyQEAAAAAnBGX8wEAAACACwhRAAAAAOACQhQAAAAAuIAQBQAAAAAuIEQBAAAAgAsIUQAAAADgAkIUAAAAALiAEAUAAAAALvCzugAAQPN28cUXq6CgQH5+9f9LMgxDLVq00JVXXqkZM2bIx8dHNTU1eu211/Thhx8qNzdXgYGB6tOnj37/+9+rZ8+ejZ7P4XDo4osvlsPh0LJlyxQYGNho++LFi7Vt27ZT1pKcnKzLLrtMkpSXl6eXX375pJ+XJJvNpunTpzc8/ve//63S0tJTPufo0aMbaly1apW++eabU+4XHx+va6+99pTbAADuhRAFALDcgw8+qPHjxzc83rFjh26++WbZ7XbddtttuummmxQQEKC//vWv6tatmyorK/Wf//xHN954o15//XX17t274WeXLl2qmJgY1dXV6YMPPjgpmERHR2vMmDGnrGPdunUNf6+trdWkSZMUGxt7xv0kqW/fvkpPTz/rcwYGBuruu+8+634AAPdGiAIAuJ2uXbuqX79+2rZtm+bMmaODBw9q6dKlatGihSQpJCREU6ZMUWlpqXbu3NkoRL3xxhsaM2aMQkJC9Nprr+maa66RzWaz6q0AALwQ90QBANxKbW2t1qxZo9WrV2vIkCH64osvNGzYsIYA9WMzZ87UxIkTGx5nZWVp27ZtGj9+vK688koVFxdr+fLlTVg9AKA5IEQBACz34IMPKj09Xenp6Ro0aJAefvhh/eY3v9GNN96o4uJiRUVFndPzzJkzR+PGjVN4eLjsdruuueYavfrqqyZXDwBobricDwBgub/85S+N7on6saioKOXn559y27Fjx2S32xUQEKCSkhJ9+OGH8vHx0SeffCKpfpKJsrIybdmyRcnJyabVDwBoXjgTBQBwaxdffLG+/PJLlZWVnbTtT3/6k6ZMmSJJmjdvnuLj4/Xxxx/rgw8+0AcffKCPPvpIQ4YM0WuvvdbUZQMAvBghCgDg1q6//npFRkZqypQpysrKkmEYOnr0qP7xj39o1apVmjp1qpxOp/773/9qwoQJio2NbfTnF7/4hT755BPl5uZa/VYAAF6Cy/kAAG4tMDBQc+fO1XPPPaepU6eqsLBQQUFBSklJ0RtvvKGePXvq888/V35+vsaOHXvSz1988cVq2bKlXn/9dd17773av3//aSeb6Ny5c6Opyl988UWFhISctF9VVVWj/VatWnXa5xw2bFjD30tKSvT3v//9lPtFRUWddpp0AIB7sRmGYVhdBAAAAAB4Ci7nAwAAAAAXEKIAAAAAwAWEKAAAAABwASEKAAAAAFxAiAIAAAAAFxCiAAAAAMAFhCgAAAAAcAEhCgAAAABcQIgCAAAAABcQogAAAADABYQoAAAAAHDB/wfEgBJHI85UlAAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20027 (\\N{CJK UNIFIED IDEOGRAPH-4E3B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25104 (\\N{CJK UNIFIED IDEOGRAPH-6210}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38477 (\\N{CJK UNIFIED IDEOGRAPH-964D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32500 (\\N{CJK UNIFIED IDEOGRAPH-7EF4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21518 (\\N{CJK UNIFIED IDEOGRAPH-540E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25454 (\\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24067 (\\N{CJK UNIFIED IDEOGRAPH-5E03}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:49:08.764066Z",
     "start_time": "2025-06-10T04:49:08.761555Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "e1487adef69983bd",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Logistic模型",
   "id": "fd1f471639852266"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:49:25.273318Z",
     "start_time": "2025-06-10T04:49:08.783282Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "\n",
    "print(\"\\n训练Logistic回归模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建逻辑回归模型\n",
    "# 使用saga求解器以更有效地处理大规模稀疏数据\n",
    "logistic_model = LogisticRegression(\n",
    "    C=1.0,                # 正则化强度的倒数\n",
    "    solver='saga',        # 适用于大型稀疏数据集的solver\n",
    "    max_iter=200,         # 增加最大迭代次数以确保收敛\n",
    "    n_jobs=-1,            # 使用所有CPU核心\n",
    "    random_state=42,      # 随机种子\n",
    "    multi_class='multinomial',  # 多分类策略\n",
    "    class_weight='balanced',    # 处理类别不平衡问题\n",
    "    verbose=1             # 显示训练进度\n",
    ")\n",
    "\n",
    "# 在降维后的训练集上训练模型\n",
    "print(\"开始训练Logistic回归模型...\")\n",
    "# 使用PCA降维后的特征训练模型\n",
    "logistic_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "log_training_time = time.time() - start_time\n",
    "print(f\"Logistic回归模型训练时间: {log_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = logistic_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"Logistic回归模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "log_accuracy = accuracy_score(test_labels_encoded, y_pred)\n",
    "print(f\"\\nLogistic回归测试集准确率: {log_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\nLogistic回归详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "report = classification_report(test_labels_encoded, y_pred, target_names=class_names)\n",
    "print(report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "conf_matrix = confusion_matrix(test_labels_encoded, y_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Logistic Regression Confusion Matrix')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "df70ff4a65580d08",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练Logistic回归模型...\n",
      "开始训练Logistic回归模型...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1264: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convergence after 16 epochs took 16 seconds\n",
      "Logistic回归模型训练时间: 16.11 秒\n",
      "Logistic回归模型预测时间: 0.01 秒\n",
      "\n",
      "Logistic回归测试集准确率: 0.8319\n",
      "\n",
      "Logistic回归详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.85      0.88      0.87      1999\n",
      "          娱乐       0.79      0.79      0.79      2000\n",
      "          家居       0.79      0.82      0.80      2000\n",
      "          教育       0.91      0.89      0.90      2000\n",
      "          时政       0.75      0.78      0.77      2000\n",
      "          游戏       0.85      0.84      0.85      2000\n",
      "          社会       0.84      0.84      0.84      2000\n",
      "          科技       0.89      0.85      0.87      2000\n",
      "          股票       0.79      0.79      0.79      2000\n",
      "          财经       0.86      0.82      0.84      2000\n",
      "\n",
      "    accuracy                           0.83     19999\n",
      "   macro avg       0.83      0.83      0.83     19999\n",
      "weighted avg       0.83      0.83      0.83     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# svm模型",
   "id": "a7928ea3093061a4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:50:20.637114Z",
     "start_time": "2025-06-10T04:49:25.309597Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "print(\"\\n训练SVM模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建SVM模型\n",
    "# 使用LinearSVC（线性SVM）因为它对大规模数据效率更高\n",
    "# 为了得到概率输出，使用CalibratedClassifierCV包装\n",
    "base_svm = LinearSVC(\n",
    "    C=1.0,               # 正则化参数\n",
    "    dual=False,          # 对于样本数 > 特征数的情况，设为False更高效\n",
    "    max_iter=1000,       # 增加迭代次数确保收敛\n",
    "    random_state=42,     # 随机种子\n",
    "    class_weight='balanced',  # 平衡类别权重，处理类别不平衡问题\n",
    "    verbose=1            # 显示训练进度\n",
    ")\n",
    "\n",
    "# 包装为校准分类器以获取概率输出\n",
    "svm_model = CalibratedClassifierCV(\n",
    "    estimator=base_svm,\n",
    "    cv=3,                # 交叉验证折数\n",
    "    method='sigmoid'     # 使用Platt缩放\n",
    ")\n",
    "\n",
    "# 在降维后的训练集上训练模型\n",
    "print(\"开始训练SVM模型，这可能需要一些时间...\")\n",
    "# 使用PCA降维后的特征训练模型\n",
    "svm_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "svm_training_time = time.time() - start_time\n",
    "print(f\"SVM模型训练时间: {svm_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "svm_pred = svm_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"SVM模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "svm_accuracy = accuracy_score(test_labels_encoded, svm_pred)\n",
    "print(f\"\\nSVM测试集准确率: {svm_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\nSVM详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "svm_report = classification_report(test_labels_encoded, svm_pred, target_names=class_names)\n",
    "print(svm_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "svm_conf_matrix = confusion_matrix(test_labels_encoded, svm_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(svm_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.title('SVM模型混淆矩阵')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "1b8b58d593a36b6d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练SVM模型...\n",
      "开始训练SVM模型，这可能需要一些时间...\n",
      "[LibLinear][LibLinear][LibLinear]SVM模型训练时间: 54.94 秒\n",
      "SVM模型预测时间: 0.06 秒\n",
      "\n",
      "SVM测试集准确率: 0.8254\n",
      "\n",
      "SVM详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.83      0.89      0.86      1999\n",
      "          娱乐       0.79      0.77      0.78      2000\n",
      "          家居       0.78      0.82      0.80      2000\n",
      "          教育       0.90      0.89      0.90      2000\n",
      "          时政       0.75      0.77      0.76      2000\n",
      "          游戏       0.85      0.83      0.84      2000\n",
      "          社会       0.83      0.85      0.84      2000\n",
      "          科技       0.88      0.85      0.86      2000\n",
      "          股票       0.79      0.78      0.79      2000\n",
      "          财经       0.85      0.81      0.83      2000\n",
      "\n",
      "    accuracy                           0.83     19999\n",
      "   macro avg       0.83      0.83      0.83     19999\n",
      "weighted avg       0.83      0.83      0.83     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2007193626.py:75: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "raw",
   "source": "",
   "id": "efd04477689a32f2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:50:20.668333Z",
     "start_time": "2025-06-10T04:50:20.665789Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "61e4ff645634086",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 随机森林",
   "id": "3f8f5144b32bae99"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:19.184637Z",
     "start_time": "2025-06-10T04:50:20.709208Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"\\n训练随机森林模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建随机森林模型\n",
    "# 注意：随机森林对于大规模稀疏特征可能较慢，需要调整参数\n",
    "rf_model = RandomForestClassifier(\n",
    "    n_estimators=100,     # 树的数量\n",
    "    max_depth=None,       # 树的最大深度，None表示不限制\n",
    "    min_samples_split=2,  # 分裂内部节点所需的最小样本数\n",
    "    min_samples_leaf=1,   # 叶节点所需的最小样本数\n",
    "    max_features='sqrt',  # 寻找最佳分割时考虑的特征数量\n",
    "    bootstrap=True,       # 是否使用bootstrap抽样\n",
    "    n_jobs=-1,            # 使用所有CPU核心\n",
    "    random_state=42,      # 随机种子\n",
    "    class_weight='balanced',  # 平衡类别权重\n",
    "    verbose=1             # 显示训练进度\n",
    ")\n",
    "\n",
    "# 在PCA降维后的训练集上训练模型\n",
    "print(\"开始训练随机森林模型，这可能需要一些时间...\")\n",
    "rf_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "rf_training_time = time.time() - start_time\n",
    "print(f\"随机森林模型训练时间: {rf_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "rf_pred = rf_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"随机森林模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "rf_accuracy = accuracy_score(test_labels_encoded, rf_pred)\n",
    "print(f\"\\n随机森林测试集准确率: {rf_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\n随机森林详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "rf_report = classification_report(test_labels_encoded, rf_pred, target_names=class_names)\n",
    "print(rf_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "rf_conf_matrix = confusion_matrix(test_labels_encoded, rf_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(rf_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.title('随机森林模型混淆矩阵')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "id": "c0b6c07849f417b8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练随机森林模型...\n",
      "开始训练随机森林模型，这可能需要一些时间...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   10.8s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   57.4s finished\n",
      "[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=20)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 38543 (\\N{CJK UNIFIED IDEOGRAPH-968F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 26426 (\\N{CJK UNIFIED IDEOGRAPH-673A}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 26862 (\\N{CJK UNIFIED IDEOGRAPH-68EE}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 26519 (\\N{CJK UNIFIED IDEOGRAPH-6797}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\2370666204.py:68: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林模型训练时间: 57.57 秒\n",
      "随机森林模型预测时间: 0.10 秒\n",
      "\n",
      "随机森林测试集准确率: 0.7961\n",
      "\n",
      "随机森林详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.77      0.85      0.81      1999\n",
      "          娱乐       0.74      0.70      0.72      2000\n",
      "          家居       0.78      0.80      0.79      2000\n",
      "          教育       0.87      0.87      0.87      2000\n",
      "          时政       0.68      0.72      0.70      2000\n",
      "          游戏       0.81      0.83      0.82      2000\n",
      "          社会       0.78      0.82      0.80      2000\n",
      "          科技       0.90      0.82      0.85      2000\n",
      "          股票       0.76      0.79      0.77      2000\n",
      "          财经       0.90      0.77      0.83      2000\n",
      "\n",
      "    accuracy                           0.80     19999\n",
      "   macro avg       0.80      0.80      0.80     19999\n",
      "weighted avg       0.80      0.80      0.80     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38543 (\\N{CJK UNIFIED IDEOGRAPH-968F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26426 (\\N{CJK UNIFIED IDEOGRAPH-673A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26862 (\\N{CJK UNIFIED IDEOGRAPH-68EE}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26519 (\\N{CJK UNIFIED IDEOGRAPH-6797}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQcAAASmCAYAAACTCh3JAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAD0a0lEQVR4nOzdd3iN9//H8deJJBIhYlVCqyVozdpbqVGbij2rA1Wj9i46KLpQW9VWoyhq701kWDGjVFqzEiORiCTn90fqfJ1GS/uT3Ce5n4/rOtdVn/s+x/v0dt/nPq/zud+3xWq1WgUAAAAAAADAdJyMLgAAAAAAAACAMQgHAQAAAAAAAJMiHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACTcja6AAAAkPpMmTJFkZGRf7u8UqVKqlq1qiTp6NGj2rhx49+ua7VaNXjw4P/8+vwdyf93AAAAIO0iHAQAAP9ayZIlValSpb9dvn//ftt/R0VFadCgQU+17n95ff6O5P87AAAAkHZxWTEAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYlLPRBQAAgNRn/fr12rNnz98uz5cvn+2/b9++rXHjxv3tupcvX1alSpX+8+vzdyT/3wEAAIC0y2K1Wq1GFwEAAAAAAAAg5XFZMQAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJEQ4CAAAAAAAAJuVsdAEpzb38AKNLwFO4vGOs0SXgKaR35vcFh2cxugA8jZjYBKNLwBO4uXC8Sw2sRheAJ3Ky8MEEPCvsTo7PzXSJy+O5l+xhdAnJJjp4stElPBOc6QIAAAAAAAAmRTgIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdEeEwAAAAAAAMnDwrw0R8cWAgAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKToOQgAAAAAAIDkYbEYXQGegJmDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQ9BwEAAAAAAJA8LMxLc3RsIQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKW5IAgAAAAAAgORhsRhdAZ6AmYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFD0HAQAAAAAAkDwszEtzdGwhAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgeFovRFeAJmDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdFzEAAAAAAAAMnDwrw0R8cWAgAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACT4oYkAAAAAAAASB4Wi9EV4AmYOQgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJ0XMQAAAAAAAAycPCvDRHxxYCAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApOg5CAAAAAAAgORhsRhdAZ6AmYMAAAAAAACASREOAgAAAAAAACaVKsLBhIQEo0sAAAAAAAAA0hyH7zm4d+9eRUZGqmzZssqaNassXKsOAAAAAAAAPBMOHQ4GBAQoNDRUjRo10v379xUREaEsWbIQEAIAAAAAAKQGllRx0aqpOewW8vf318mTJ9WpUydly5ZN6dKlU2RkpMLDw2W1Wo0uL1ll9/LQiR8HqWqpfLaxovl9tH5yF13f/qkubhihcR82Urp0/9t8fdtX16lVQ3Rt+6da920XFc6X07bM08NNU4Y0168bRips0yjN+KilMmd0S9H3ZCanT53U++90UK2q5dWg9mv6evwYxcbGSpJOHD+qdzq00uuVSqtpg9pas2qFwdWa05kzp/V+53dUrXJ51apeRcOHDlJERIQkaekPi9S4QR1VKldKjRvU0ZLFCw2uFuHh4Wpc7w0F+B+yje3ZvUutmzdV5XKl1NKvibZv3WJgheZ18cIv6v1BZ9V+rbzerF9Dc7+bbmsFEnL8qN7t2Eo1KpeWX8PaWvMTxzujcMxzfP6HDqpj25aqWqG0alevonFjPlVMTIwkacyno1S+VDFVLlfK9lixfKnBFZtbeHi4GtWrrcOPfC49dOPGddV4rZJW/7TSgMrwVxd+Oa9uXd5VlYplVLdWdc2aMY2WVQ5m3c9rVKFMSbtH6VeLqkyJokaXBpiKQ4aDAQEBOn36tDp27Ggby5kzp9zd3RUVFZWmA8KKxV/Szu96yPeF7LaxbJkzaP3kLtp++Jxy1R6p1975VvWqFFLP1lUlSR+0rKw+Harr7RGLlav2SP28O0Qbp76vbJkzSJJmftRSxfL7qPJbE/TKm2Pk6pxOS8e/Zcj7S+sSEhLUv1c3vV7rDW3edUBzFi7ToQP7tHDubN25c1t9e76v+g2baMvuQxo28lNN+GqsQk4cM7psU4mJiVGPbp31aokS2rpzj378aa1u37qlUR8N0a6d2zV18iSNHf+V9vsH6fNxX2rC11/osP9Bo8s2rSNBQerUrrXCwi7Zxk6dDFHfXj3UsnVb7drvr8HDPtKIYYPtwkMkv3v3otSne2fl9PHRmk07NO27Bdq6eaPmzJqWeLzr9b7qNWiizbsOaeiITzWR450hOOY5vojwcH3Yvauat2ytXfsPa/HylQo87K85s2dKkkJOHNfwkZ9on3+Q7dGsRSuDqzav4KBAvdWuld3n0kMJCQkaOqi/bt2KMKAy/NW9e1H6oOt78vbx0ZZtu/X9/EXavHG9Zk6fanRpeESDho11MCDY9li9bqOyZPHSqE9HG10aYCoOFw76+/srODjYFgw+GgLmyJFDbm5utoAwrWlXv7TmftJWo6ZvtBtv36CMQi/9oS/n7VBcfIIuXYlQw54ztWLrUUlSqzolNXXZXh08/qvi4xM0bfk+3bwVJb+ar8o9vYsavlZEQ779Wb9dv62o6FgNmrhW1Urn18svPWfE20zT7t65oz/+uCGr1Wr7t2uxWJTezU07tm5R5sxeat6qrZydnVWmXAXVqddQK5b+YHDV5nL1ymUVLPiKurzfXS4urvLyyqJmLVopKDBA1arX0PrN21S4SFHFxcUpIiJCFotFmTJ5Gl22Ka1ZvUpDBvVX91697cY3b9qokqVKya95Czk7O6tU6TKq16CRli9dYkyhJnU0OEjhEeHqP3i43N0zyCdXbnV6t6tW/rhEO7ZufvzxbhnHu5TGMc/xZcmaVVt37VPjN/1ksVh0+9Yt3Y+NVZYsWRUbG6vQc2dVuAgzaBzBmtWrNHRQf/Xo1eexy2dMm6KcOb2V09snhSvD4wQHBSo8/KaGDh8h9wwZlCtXbr3XpZuWL/0hzU40Se2sVquGDR6gqq9VV8NGTYwuBzAVhwsH3d3dVbZsWbtg5VHPPfecvL29de7cOd27d8+IEpPN1oNnVbjZWP34Z+j3UJnCLyjkl6uaNMhPF9aPUMiKwWpdt5R+u35bkpTOyUn3omPtnpNgterll3LIyckii0W6F/O/5QkJif9vX36RcPBZy+zlpdbt3tKkr8frtfIl1LhuDeV58SW1af+WLvwSKt/8Be3Wz5vPV+fOnjaoWnN6KW8+TZk+S+nSpbONbd2ySYUKF5EkeXhk1MULv6hCmVfV84Muat6ytV4pVNiock2tUuUqWrths+rUq283nhAfL3d3d7sxJycnXbjwS0qWZ3oJCQlycXGRs7OLbcziZFH4zZs6fjT4sce7UI53KY5jXurg4ZFRklSvVnW19Gus7NlzqMmbfjp75rTi4uI0bcok1apWWW82rKO5s2dxWaRBEj+XtiT5XJKkw/4HtWnjOg39aKQBleFx4uMf/zl18+YfunvnjoGV4e/8vHa1zp8PVf9Bg40uBc+axZJ2H2mEw4SDFy9elCQVK1ZMPj4+CgoKUnR09GPXPXbsmNavX2/r45ZWXAu/q/j4pCd7WTwzqGPDMgo4GaYCjT5T68Hz9F7TCvqw7WuSpJ92HNcHLauoeIFcck7npPeaVlDBPDnklt5FUdGx2nronD7uVk85s2ZSxgzpNaZnQ8XFxcs9vUPfjyZVSkhIUHq39Oo3aLh27A/U4h9X68Iv5zVr2mRFRUUlCTTc3NwVHZ22Qu7UxGq1asqkCdq9c4cGDBpqG8/9/As6cPiIFi5Zrk0b12vO7FkGVmle2bPnkLNz0uPU6zVr6cD+fdq6ZZPi4uJ0JChImzas0/37MQZUaV7FS5RU+vTpNe3bbxQTHa0rl3/XovlzEhdaLHL7y/EuvZt7mvtRL7XhmOf4flq3SZu27VK6dE4a0PdDRd69q9Jly6lN2w7asHWnPvv8C/2weIEWzJtjdKmm9HefS+E3b2rE8KEaM/ZLZcjgYUBleJwSJUspfXo3TZrwlaKjo3X58u+aN2e2JCmGcwaHk5CQoJnTp+m9Lu/bfjABkHIcIhz09/fXhg0bFBISIimxv2Du3Ll1+vTpJAFhYGCg9u/fr+7du8vLy8uAalPe/QdxCjgZpvlrDysuPkHHz13RtOX71KxmcUnSN4t2aeH6AC0b/5bOrhmml196TlsOndWtO4n/794d9YP+iIjSoUV9tH/ehzp04lfdjopRxN3Hh6/473Zu36qd27aoWcvWcnV1VT7fAnq36wdasfwHubu725qLPxQTE81JpEEiIyPVv28vrVu3RrPnLlCBgi/blrm4uMjFxUVFihRT23YdtWH9zwZWir8qUbKUPvt8vGZMnaxa1Spr3tzZavymnzw9MxtdmqlkyuSpr7+doZATx9Skfg0NH9xX9Ro0liSlS5dO9/9yvLsfE60MHhzvjMIxL3Vwc3NTjudyqlef/tq/b48KFymqmbPnqXTZcnJxcVHRYsXVtv1b2rxxvdGl4k9Wq1XDhgxU23YduPzbwXh6emrK9Fk6fuyo6taqroH9eqtR4zclifYJDuiw/yH9ceO6mvo1N7oUwJQMnzp2+PBhnTlzRt26dVNYWJhOnDihokWLytvbW5J06tQpFSpUSO7u7goICNCePXvUtm1b5cyZ8wmvnHacvnBd1Ur72o2lc3KyXXKdO4en5q3x16czNycuS+ekMz8N0cKfAyRJObNlUt+vftKtP8PAl196TlkyuSv49G8p+C7M4drVK0lmtDo7O8vFxUX58hfQoYP77ZZd+OW88uUvkJIlQlJY2CX1/KCLvL1zadGSFcqSJYskaeH8uTp+7KjGffmNbd3Y2Fhlzkzo5Ehu374l3/z5tXzVWtvYoH59+FKWwh48iFV8fLwmz5hj+zxauXyJ8ubzVeEixfTDwnl261/45bx8fTneGYFjnmM7eiRIH380TEtXrpaLi6ukxO3g4uKigwf2686d22resrVt/QexsUrv5mZUufiLq1evKDDAX8ePH9WM6VMkSVGRkfr8s4+1dfMmfTt1hsEVmteDB7GKi4vTrO/n2z6nli1ZrHy++ZNczQPjbd28STVq1VaGDBmMLgUwJUNnDvr7++vUqVPq0KGDJOmFF16Qu7u7bQaht7e3cuXKpbCwMO3bt0/79+83XTAoSfPW+quIr7f6tq8uJyeLivh66/0WlbR4Q6AkqUXtElr2xdvK6plBHu6u+qx7fd2Pjde6vSclSaN7NNDYDxvKxTmdfLJ7asKAplq2+YhuREQZ+bbSpAoVK+vmHzc0d/YMxcfH6/ffwjTnuxmqW7+RXq9RW+F//KEli+Yr7sEDBR4+pE0bflajJn5Gl20qd27fVpd3O+nVV0tq6ozvbF+SJalU6TLasX2rNm/coISEBB0JDtIPi+arRcs2BlaMv7r066/q0KaVzpxO7MW1acN67d61Qy1bs51SktUq9f6gs9auXimr1arTJ0M0d/YMtWrbUdVr1Fb4zaTHu4Yc71IcxzzHV6Dgy4qJidGkb77Sgwexunz5d034arze9GsuZxdnff3FWB06eEBWq1VHjwRr8aL5atacuxU7Ch+fXPIPOq69BwJsD2+fXBoyfCTBoMGsVqlb13f108ofZbVadTLkhL6bNV3tOrxldGl4jODgQJUuXdboMpBcLE5p95FGWKwG3arpwIEDCg0NtQWDCQkJcnJK/B97/vx5RUdHq2jRxFkgZ8+e1dKlS9W5c2fbjML/yr38gP9f4Skk+tAXeqPbNO0JSmywX7bICxrTs6GK+HorOuaBZq48oHFztkmSnNM56Yu+TeRXo7hcXdJp35EL6vvVT7p0JUKSlPu5zPp2cDNVLP6SYh/Ea+W2oxry7c+KuR9n2Pt7kss7xhpdwn/mf3C/ZkydpF8vXFDGjBlVp0Ejvdf1A7m4uOpUyAl9/cXnOh96VlmyZNXbnd9Xw8ZNjS75P0vvnPoOhgvmzdHXX46Tm7u7LLJvILvfP0i7dm7XlG8n6vLvv8nHJ5fefreL6jdsZFC1z0Aa6ZFbsugrmvX9PJUpV16StHzpEs39fpZuRUTopbz51KtPP5WvUNHgKv+7mNjUeXOB4MAATfxqrMIuXVSWrNnUqm0HtWjdXpJ06uQJffPo8e6999UgFR/v3FxS3/FOMt8xL7Xef/SX86H6ctwYhZw4oYyZMqp+g8bq/P4HcnV11Y/LlmjRgrm6du2asmfLrvZvva2WrdsaXfJ/5pRGmreXKPqyZn0/X2X//Fx6VL03auj9D3qoyZv8IGK0wIDD+nL85/r14gVlzZpN7Tq8pTbtOhhd1jOTRnYnSVKFMiX15TcTVKVqNaNLeabcDL9W0zG4vzbK6BKSTfTuUUaX8EwYEg5euXJF27ZtU/v2iV8grFZrkrsSnz9/XvHx8SpYMPFuh9HR0c9k+ndqCQfNLjWHg2aSGsNB00lDJ41pWWoNB80ktYaDZpNaw0EzSSvhIOAI2J0cH+FgIsJBx2fIma6np6eKFy9u+/OjweDDrNLX11dnz57VmjVrJCU2aAYAAAAAAADw7BgSDnp4eChPnjw6duyY4uPj7ZY9DAqDg4MVGhqq8uXL240DAAAAAAAglTC6LyA9B5/IsHfi5eWlPHnyKCQkJElAGBgYqJ07d6pNmzamu/kIAAAAAAAAkFIMjTkfFxAGBgZq9+7dprwrMQAAAAAAAJCSDG+P6eXlJUk6c+aM7t27px07dqhjx44EgwAAAAAAAEAyc4gLpL28vPT8888rMjKSYBAAAAAAAABIIYbPHHzI09NT1apV48YjAAAAAAAAaYUTOY+jc4iZgw8RDAIAAAAAAAApx6HCQQAAAAAAAAAph3AQAAAAAAAAMCmH6TkIAAAAAACANMbCvDRHxxYCAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApOg5CAAAAAAAgORhsRhdAZ6AmYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFLckAQAAAAAAADJw8K8NEfHFgIAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCk6DkIAAAAAACA5GGxGF0BnoCZgwAAAAAAAIBJEQ4CAAAAAAAAJkU4CAAAAAAAAJgUPQcBAAAAAACQPCzMS3N0bCEAAAAAAADApAgHAQAAAAAAgBQQHh6u2rVr69ChQ7ax06dP66233lLJkiVVqVIlff7554qLi7MtX7VqlWrXrq0SJUrIz89PwcHBtmXx8fEaN26cKlWqpJIlS6pbt266fv36v6qJcBAAAAAAAABIZoGBgWrVqpUuXbpkGwsPD1enTp1UqVIl+fv7a9myZdq5c6fmzZsnSTp06JA+/fRTjR07VocPH1bjxo3VrVs3RUdHS5KmTZumffv2acWKFdqzZ4/c3Nw0fPjwf1UX4SAAAAAAAACQjFatWqX+/furT58+duM//fSTXnrpJXXt2lUuLi56/vnn9f3336tevXqSpOXLl6tBgwYqXbq0XFxc1KlTJ2XJkkXr16+3Le/cubN8fHyUMWNGDRs2TLt371ZYWNhT10Y4CAAAAAAAgORhsaTZR2xsrCIjI+0esbGxj/3fUKVKFW3ZskX169e3Gz927JgKFiyoESNGqHLlyqpVq5bWrFkjb29vSVJoaKgKFixo95z8+fPr9OnTunv3rq5evWq3PHv27MqcObPOnDnz1JuIcBAAAAAAAAD4l2bMmKHSpUvbPWbMmPHYdXPkyCFnZ+ck47dv39bKlStVvHhx7dy5U5MnT9bSpUs1Z84cSVJUVJTc3d3tnuPm5qZ79+4pKipKkpQhQ4Ykyx8uexpJqwIAAAAAAADwj7p27aq3337bbszV1fVfvYarq6uKFSum5s2bS5JeeeUVtW/fXhs2bNC7774rd3d3xcTE2D0nJiZGWbJksYWGD/sPPrrcw8PjqWtg5iAAAAAAAADwL7m6uipjxox2j38bDvr6+ia5FDkhIUFWq1WSVKBAAZ07d85ueWhoqAoUKKDMmTMrZ86cCg0NtS27ceOGbt26leRS5H9COAgAAAAAAIDkYXFKu49noFmzZjp79qxmzZql+Ph4nTlzRgsXLlSTJk0kSc2bN9fatWt18OBBPXjwQHPnztXNmzdVu3ZtSZKfn5+mTZumsLAwRUZGasyYMSpXrpzy5Mnz1DVwWTEAAAAAAABgAF9fXy1cuFDjx4/XzJkz5ebmpjZt2qhDhw6SpIoVK2rkyJEaNWqUrl27pvz582vWrFny8vKSJHXv3l1xcXFq166doqKiVL58eU2YMOFf1WCxPpynaBLu5QcYXQKewuUdY40uAU8hvTOTjx2exegC8DRiYhOMLgFP4ObC8S41MNVJbSrlZOGDCXhW2J0cnxvTsSRJ7nW/NrqEZBO9sa/RJTwTnOkCAAAAAAAAJkWODQAAAAAAgOTBNFeHx8xBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgeFualOTq2EAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFDckAQAAAAAAQPKwWIyuAE/AzEEAAAAAAADApEw3c/C37Z8bXQKewss9VxhdAp7C2cnNjC4BT+DqzG9AqUE6J35NdXThUbFGl4CnwDHP8WVyczG6BDwFq6xGl4CnYBHnDwCeDc6gAAAAAAAAAJMy3cxBAAAAAAAApBAL89IcHVsIAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAk6LnIAAAAAAAAJIHPQcdHlsIAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKG5IAAAAAAAAgeVgsRleAJ2DmIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFz0EAAAAAAAAkDwvz0hwdWwgAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACToucgAAAAAAAAkofFYnQFeAJmDgIAAAAAAAAmRTgIAAAAAAAAmBThIAAAAAAAAGBS9BwEAAAAAABA8rAwL83RsYUAAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKS4IQkAAAAAAACSh8VidAV4AmYOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFL0HAQAAAAAAECysNBz0OExcxAAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACToucgAAAAAAAAkgU9Bx0fMwcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKS4IQkAAAAAAACSB/cjcXjMHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCk6DkIAAAAAACAZGGx0HTQ0aWKmYMJCQlGlwAAAAAAAACkOQ4/c3Dv3r2KjIxU2bJllTVrVhJnAAAAAAAA4Blx6JmDAQEBCg0NVdmyZXX//n1FRETIarUaXRYAAAAAAACQJjhsOOjv76+TJ0+qU6dOypYtm9KlS6fIyEiFh4ebNiC8eOEX9f6gs954rYKa1q+pud/NsF1yvWXTerXxa6RaVcup1Zv1terHpQZXm/Zly5heh8bUU6WXcyRZ9lxmN4V83VitKr9kN96q0os6NKaeLkz10+aPaqmMbzZJUu6sGXRhSlO7x6/T/HR9dkvbOng2Nq5bq+oVS9s9KpcpriplX5UkLV+yWM0a1VX1iqXVrFFdLV+yyOCKER4ersb13lCA/yHb2J7du9S6eVNVLldKLf2aaPvWLQZWaF63b9/SyOGDVKtaBdWsWl79e/fQHzeuS5KWL1mkZo3qqFrF0mrWqI6WsS+luFsR4erQvIGOBB62jW3fskFvt2qiRjUqqmOLRlq7cpltmdVq1YLvZ6hd07pqVKOi3mvXTLu2bzaidFOJiAhX6zfrKTjA3za2c9sWvd22mepUK68Wjd7QnJlT7drstG/eSLUql9EbVcvaHhcvnDeifFMKDw9Xo3q1dfiRz6WHbty4rhqvVdLqn1YaUBke9bjzh9GfjFK5ksVUqWwp22PFcr43OYr4+Hi926mDPho62OhS8IxZLJY0+0grHPKy4oCAAJ0+fVodO3a0jeXMmVM3btxQVFSUJJnuEuN796LUp3sXlatYSZ9/NVG3Im5pYO/uio+PV/WatfX5xyM0afpsFS3+qo4fDVaPLm8rb778KlGqtNGlp0nl8mfTt++UU96cmZIss1ikaZ3LK2smV7vxSi/n0Ji2pdRmwm4FXQjXuzUKaH7Pyio9cJ1+D7+nvN1X2dZN52TR0r6v6dKNKAWcv5ns78dM6jZopLoNGtn+fP3aNb3dvqV69O6nPbt2aObUSZo0fbYKFS6ikyeO6/13Oyqvb36VKVvewKrN60hQkEYMG6ywsEu2sVMnQ9S3Vw8NGT5Cjd9sqmNHj6jXB13l6empMuXYTilpcL8PlcnTU6vWbpZTunT6ZMQQjf5khPyat9KMqd/q20f2pa7vdlA+9qUUc+JosMZ9OlyXfwuzjV04f05fjR6pLybPUuGiryrk2BH16/6uXsznq+IlSmvl0oXa9PNPGvP1VOV5Ka8O7N2lT4cPUM6cPnqlSDED303adexIkMaMGqbfH9lOZ06F6LMRQ/Tx2C9VsfJruvTrBQ388AO5Z8ig1u07KSoyUpd+vahlazbJ2yeXgdWbU3BQYJLPpYcSEhI0dFB/3boVYUBleNTjzh8kKeTEcQ0f9YkaN2lqUGX4J9OnTlZQYIBy5cptdCmA6TjczEF/f38FBwfbgsFHZwnmyJFDbm5uioqKUnh4uFElGuJYcJAiIsLVf/BwubtnkE+uXHrr3S5a9eMSXfr1guLj42S1Jvz5/8sip3Tp5Jre9Ymvi3+vVaUXNa1LBY1ZdeKxy/s3KqIrEdH6PTzabrx91Xxa5X9J/qE3FRdv1YwtZxV+N1ZNyr6Q5DX6NiysHJ5uGrwoKFneAxJZrVaNGj5YlatUU70GjVW12uv6acM2FSpcRHFxcYkn9xaLMmVKGgIj+a1ZvUpDBvVX91697cY3b9qokqVKya95Czk7O6tU6TKq16CRli9dYkyhJnXqZIhOHD+qEZ98rkyenvLw8NDQEZ+ox4f9VLXa61rNvmSYTetWa/TIwXqna0+78d8u/ar4+HhZE6yJ5wsWi5yc0snVNb0k6e7dO2r/zvt6MW8+WSwWVapaXXlezKsTx44Y8C7Svg0/r9Ynwwep8we97MavXL6sJs1aqnLV6nJyctJLeX1VtXpNHQ0KlCSdOX1Snpm9CAYNsGb1Kg0d1F89evV57PIZ06YoZ05v5fT2SeHK8Ki/O3+IjY1V6LmzKlKkqDGF4R8dOnhAW7dsVq3abxhdCmBKDjdz0N3dXWXLlpXVan3sNM3nnntOsbGxCgoKkru7uzJkyGBQpSkrPiFBLi4ucnZ2sY05OTkp/OZNFS32qooUe1Vd326vdOnSKT4+Xj36DFBhfuVPFjtOXNOPBy8pPsGqWe9XtFtW+eUcerPcC3rj063a9Ukdu2Uv5/bU4j0X7MbOXrmjIi942Y29lMNDPeu/oqbjdyg2jjt1J6cN69bql/Oh+mLCZNuYh4eHfr14QW2aNVZ8fLzadnhLL79S2MAqzatS5Sqq36CRnJ2dNXhAX9t4Qny83N3d7dZ1cnLShQu/pHSJphZy4pjy5vPV6pXLtWL5EkVH31PFSlX1Yb+Bkv63L7Vu1ujPfakT+1IKKVuhsmrVaaB0zs767KOBtvEyFSqpUNHi6tWlo5zSpVNCfLy69uynVwonflHu1Lm73ev8euEX/XrhvAq+UihF6zeLchUqq3bdBnJ2dtaooQNs49Vr1lb1mrVtf74fE6MDe3erdr0GkqRTISfk5uamHl066eL5UHnnyqW3u3ygylWrp/RbMJ1HP5cGDbAPCA/7H9Smjeu0eOkKNXuz0d+8AlLC350/nD1zWnFxcZo6eZKOBAUpY6aMetOvud56+105OTncnBlTuXnzpkaNGKYJk6Zq4fy5RpcDmJLDHAUvXrwoSSpWrJh8fHwUFBSk6Ojox6577NgxrV+/XrGxsSlYobGKlyip9OnTa9q33ygmOlpXLl/WovnfJy60WJQrd25NnPadduwP1BcTp2r29Mk6dGCfsUWnUdfvxCg+IWnfy+yZ0mviO+XUbdYhRd2PS7I8Y3pn3YuNtxuLjo2TR3r7jP7DBoW07dgVBf5irtmxKS0hIUHfz5ymt9/rIg8PD7tluXM/r90HgzR30TJt2bhB8+d8Z1CV5pY9ew45Oyf9Dev1mrV0YP8+bd2ySXFxcToSFKRNG9bp/v0YA6o0rzu3b+vcubO6dOlXLViyUguXrtKN69c0avj/+gTlzv289hwM1txFy7Vl43rNmzPLwIrNI2u27Er3mH3nQewDefvk1vhJM7Vhl79GfzVZ876bqoBD+5OsG3bpoob2/UA16zZU8ZJlUqJs08mWPftjj3GPuhcVpSH9eym9W3q1apt4VY3FIr1SuKgGDf9YqzbuUMu2HfXRwD4KOX40Jco2tb/7XAq/eVMjhg/VmLFfKkMGj8c8Eynp77ZT5N27KlO2nNq066CN23Zq9Ngv9MOiBVowd44BVeKhhIQEDR08QB3eelsvv/KK0eUApuUQ4aC/v782bNigkJAQSYn9BXPnzq3Tp08nCQgDAwO1f/9+de/eXV5eXgZUa4xMmTz11bczdPLEMb1Zv6Y+GtxX9Ro0liR9N32yXF3Tq2z5inJ2cVHlqtVUq259rV6x7Amvimdpynvl9d22czr26+P7zNyLjZe7azq7MXdXZ0XG/C9I9EjvrKbl8mjm1nPJWiukwMOHdPOPG2rctFmSZc4uLnJ2cVGhIkXVqm0HbdqwzoAK8XdKlCylzz4frxlTJ6tWtcqaN3e2Gr/pJ0/PzEaXZiquromtK/oOGCIPDw9ly5Zd3Xr01v69u3XvXmJ/4If7UmH2JYcwb9ZUuaZPr9LlKsjZ2UUVKr+mGrXr6edVy+3W279np3q+115VqtdS/6GjDKkV0qWLF/T+2+0UHx+vSdO/V4Y/f8hq2/EdfTb+G72Q50W5uLjojXoNVbpcBe3cxs1jjGC1WjVsyEC1bddBhblc1aFVqFRZM7+fpzJly8nFxUVFixVXu/ZvadPG9UaXZmqzZ81QeldXtW3XwehSkIyMvmkINyR5MsMvKz58+LDOnDmjbt26KSwsTCdOnFDRokXl7e0tSTp16pQKFSokd3d3BQQEaM+ePWrbtq1y5sxpcOUp68GDWMXHx+nbGXNs/wBXLl+ivPl8FREenuRLsbOzs5xdXB73UkgGubNmUMWXc6hUvqzq1yjxsrlMbi4a166UGpV+Xu0n7dXp32/rlVyeds8r6OOprceu2P5cs5i3bkbe14GzN1K0fjPasXWLqtWoJXf3/7Um+GHBPJ04flSjx39tG4t9EEvo5GBu374l3/z5tXzVWtvYoH59+FKWwvLm85U1IUEPHjxQ+vSJPeviExJnR/+wcJ7Oh57TmPHf2NZ/8CBWmdmXDHX92hVlesL5woLvZ2jpwjnqM+gj1azTIKVLxJ8O7N2tj4cNVKOmzdS1Rx+7WVA/LJijAi8XUplyFWxjifuhmxGlmt7Vq1cUGOCv48ePasb0KZKkqMhIff7Zx9q6eZO+nTrD4Arx0I5tW3Xz5h9q3rK1bSz2Qazc3Nh3jPTz2tW6cf26qlRInKUeHZ14JciO7Vu192CAkaUBpmLozEF/f3+dOnVKHTok/krwwgsvyN3d3TaD0NvbW7ly5VJYWJj27dun/fv3mzIYlCSrVerzQRf9vHqlrFarTp8M0bzZM9WybQdVrfa6tm3ZqIP798pqtSo48LA2rf9Zdeo1NLps0/g9/J7yvL9CBXr+ZHv8Fn5PgxYFqf2kvZKkxXsvqFmFF1X55RxyTmdRl1oFlCNzeq0P/t32OuUL5NChs38Y9TZM5eiRIJUsZX+pXInSZbRrxzZt3bRBCQkJOhocpKWLF6hZi1YGVYnHufTrr+rQppXOnE7sHbRpw3rt3rVDLVu3Mbo0UylfoZJy535en40apnv3ohQRHq5pkyeq2us1ValKNe3asU1bHtmXlixeIL8WrZ/8wkg2FatW186tm3T44D5ZrVYdDQrQ1o3rbCHg8sXztXzxfH0zbQ7BoIFCjh/VsAEfqmffgeree0CSyyOvX7uqb8aN1uXfwhQXF6d1q1fqxNEjqtuwiUEVm5uPTy75Bx3X3gMBtoe3Ty4NGT6SYNDBWK1WfTV+rA4dPJB4DDwSrMUL53OeZ7DVP2/Ufv8g7T0YoL0HA1S/QUPVb9CQYBBIYYbNHDxw4IBCQ0NtdyVOSEiQk5OTfH19df78ebsZhHfu3NH27dvVuXNnUwaDUuLlW2O//laTvhqniV+OlVfWbGrf6V018WshSYqJidGELz7XH3/cUE5vHw0YMkKVX6tubNGws+fUdQ1aGKjxHUorVxZ3nb58R22+2aNbUf/rnfliDg+duXzHwCrN4/ffwpTjuefsxgoVLqLPv5yg6VMmafQnI+Tjk0t9BwxRrTr1DKoSj1Os+Kvq03+g+n7YXbciIvRS3nyaMHmafPMXMLo0U3F2cdH02Qs04auxata4rmLvx6pqtdfVb+BQZfL01NgvJ2ralIka88lH8vbJpX4Dhqo2+5Kh6jf20/2YGE3+eqzC//hDz3l768OBw1WxSjVZrVYt+H6GYqKj1fv9TnbPa/vWe2rXqbMxRZvQgu9nKS4uThO//FwTv/zcNl68ZGl9OWm6uvXqJ4vFSd07v6XIu3eVN5+vvpg4Vc+/kMfAqgHHV6NWbfUbOFiff/axrl27puzZsqtb955q0Kix0aUBgOEsVqs16Z0VktmVK1e0bds2tW/fXpJsdyZ+1Pnz5xUfH6+CBQtKkqKjo5PcnfK/uBmV9EYRcDyFeq00ugQ8hbOTk/brg2NxdXaI1rJ4ggdxKf5RjH/pbswDo0vAU+CY5/gyudH2JjWwis+l1MApDfU7S6vcDG/k5hgyt1lgdAnJ5vYPaaNfpiFnUJ6enipevLjtz48Ggw+zSl9fX509e1Zr1qyRJHpBAAAAAAAAAM+YIeGgh4eH8uTJo2PHjik+Pt5u2cOgMDg4WKGhoSpfvrzdOAAAAAAAAIBnw7BrL7y8vJQnTx6FhIQkCQgDAwO1c+dOtWnTxrQ9BgEAAAAAAIDkZugV8F5eXpKkkJAQFSlSROnSpVNgYKB2795t2rsSAwAAAAAApBlcCOrwDG+P+TAgPHPmjO7du6cdO3aoY8eOBIMAAAAAAABAMnOIW7p5eXnp+eefV2RkJMEgAAAAAAAAkEIMnzn4kKenp6pVq8aNRwAAAAAAAIAU4jDhoMQdiQEAAAAAANISsh7H5xCXFQMAAAAAAABIeYSDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmJRD3ZAEAAAAAAAAaQc3JHF8zBwEAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApOg5CAAAAAAAgGRBz0HHx8xBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgW9Bx0fMwcBAAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACT4oYkAAAAAAAASB7cj8ThMXMQAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAk6LnIAAAAAAAAJKFxULTQUfHzEEAAAAAAADApAgHAQAAAAAAAJMiHAQAAAAAAABMip6DAAAAAAAASBb0HHR8zBwEAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApOg5CAAAAAAAgGRBz0HHx8xBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIpwEAAAAAAAADApbkgCAAAAAACA5MH9SBweMwcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAABIAeHh4apdu7YOHTqUZNn169dVqVIlrVy50m581apVql27tkqUKCE/Pz8FBwfblsXHx2vcuHGqVKmSSpYsqW7duun69ev/qibCQQAAAAAAACQLi8WSZh//VmBgoFq1aqVLly4lWZaQkKD+/fsrIiLCbvzQoUP69NNPNXbsWB0+fFiNGzdWt27dFB0dLUmaNm2a9u3bpxUrVmjPnj1yc3PT8OHD/1VdhIMAAAAAAABAMlq1apX69++vPn36PHb5lClT5O3tLR8fH7vx5cuXq0GDBipdurRcXFzUqVMnZcmSRevXr7ct79y5s3x8fJQxY0YNGzZMu3fvVlhY2FPXRjgIAAAAAAAA/EuxsbGKjIy0e8TGxj523SpVqmjLli2qX79+kmUHDx7UunXrNHLkyCTLQkNDVbBgQbux/Pnz6/Tp07p7966uXr1qtzx79uzKnDmzzpw589Tvg3AQAAAAAAAA+JdmzJih0qVL2z1mzJjx2HVz5MghZ2fnJOM3b97U0KFD9eWXX8rDwyPJ8qioKLm7u9uNubm56d69e4qKipIkZciQIcnyh8ueRtKqAAAAAAAAgGfgv/TmSy26du2qt99+227M1dX1qZ9vtVo1cOBAdejQQUWLFn3sOu7u7oqJibEbi4mJUZYsWWyh4cP+g48uf1zQ+HdMFw66pmOyZGpwdnIzo0vAU/BpOtHoEvAEN9c8vp8FHItVVqNLwBN4ZXj6kzwYxyntfvdIM6xWjnepwf24BKNLwFNI78J3W8fHB1Na5+rq+q/CwL+6cuWK/P39dfToUU2ZMkWSFBkZqY8//libNm3SjBkzVKBAAZ07d87ueaGhoXrttdeUOXNm5cyZ0+7S4xs3bujWrVtJLkX+J6YLBwEAAAAAAACj5cqVS8ePH7cbq1Gjhnr06CE/Pz9JUvPmzdW9e3fVq1dPpUuX1qJFi3Tz5k3Vrl1bkuTn56dp06apWLFiypIli8aMGaNy5copT548T10H4SAAAAAAAADggCpWrKiRI0dq1KhRunbtmvLnz69Zs2bJy8tLktS9e3fFxcWpXbt2ioqKUvny5TVhwoR/9XdYrCab2383hinyqUG8uf5ZplpcVuz4uKw4dYiN57PJ0Tk7celWasBlxcCzwWXFqQOXFTu+DC58MEmSd+cfjS4h2Vyd1dzoEp4JZg4CAAAAAAAgWaTlG5KkFfzUAAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEnRcxAAAAAAAADJgp6Djo+ZgwAAAAAAAIBJEQ4CAAAAAAAAJkU4CAAAAAAAAJgUPQcBAAAAAACQPGg56PCYOQgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJ0XMQAAAAAAAAycJioemgo2PmIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFDckAQAAAAAAQLLghiSOj5mDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQ9BwEAAAAAAJAs6Dno+Jg5CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEnRcxAAAAAAAADJg5aDDo+ZgwAAAAAAAIBJEQ4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUtyQBAAAAAAAAMnCYuGOJI6OmYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFD0HAQAAAAAAkCzoOej4mDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdFzEAAAAAAAAMmCnoOOj5mDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmFSqCAcTEhKMLgEAAAAAAABIcxz+hiR79+5VZGSkypYtq6xZs9LIEgAAAAAAIJUgx3F8Dj1zMCAgQKGhoSpbtqzu37+viIgIWa1Wo8sCAAAAAAAA0gSHnTno7++v06dPq1OnTpKka9euKTIyUlar1bQzCDdvXK+Phg6Uq2t621j1GjX16ZjxWrZkkX5YNF9/3PhD2XNkV+u2HdWqTTsDqzWnjevWauxno+zGHjx4IIvFor2Hj2r5ksVasmi+bv5xQ9my51Drdh3UojXbKblkz+yund+0UbcJm7Xn2G+SpIk9auqtN4roQfz/2hUMmrlL3284Lknq27yMOjd8VVkzuSng7FUNmL5TJ3+9KUlycrLos3eqqm3NQsqQ3kU7j15Sr2+36Wp4VMq/ORM4c+a0vvlyvE6dDJGLi4sqVKqsfgMGK0uWLBr96SitXrVCzs4utvX7DRikZi1aGVixuTzpeLd962Z9P3Oafv/9N2X2zKyGTZrqnS7d5OTk0L9Lpkn+hw5q8sSvdfHCebm5uavWG3XUq88Aff3F51r/81q7de/fj1G5ChU1Zfpsg6o1p7873k35dsJjt1H5ChU1dQbbKKX90+fS0h8WadHC+frjxg1lz5FDbdt1UOu27Y0u2XQ2rl+rcX/z2bTH/6j279ml6VMm6rewS8qV+wW99353Va9Ry5hiTezM6cfsSwMT96WHjh4JVpd33tKhoGMGVgqYh8XqgFPxAgICdPLkSXXs2NFu/MaNG4qOjpaHh8d/DgjvxqTe/oUTvhqv27duaeSnY+zGd+/coVEfDdGUGbNVqHARhZw4ri7vdNDEyTNUplx5g6r9/4l3vH+W/8n1a9f0dvuW6tG7nzJmzKRPPhqiSdMTt9PJE8f1/rsd9fXk6SpTNnVuJ5+mE40u4W9VLJxLs/rXlW8uL70xcJktHNw7qa2mrTmiRVtPJnnOB01KakjbCmox6icdPnNVXRq+qiFtK6hkl7m6eSdGQ9tVUJPKBdRsxCrdvherKb1qKWMGV/mN+CmF393Tu7mmj9El/CcxMTFqVL+2/Jq10Htd3ldUVJQ+GjpYTk4WTZw8XW1bNVPrtu3VuElTo0t9JmLjU+9n00OPHu9eyuurru900JjxX6tSldf068UL6tPjfbVo3VbtOr5tdKn/iXMqDTUjwsPVoM7rGjx8pBo2elM3b/6h7l3fVY2atfV+91526x7Yv1fDBvXTrDkL5Zu/gEEV//84pcLfjp90vHvUgf17NWRgP82em3q3UWr1T9vJr3lLjRg2RNNmzlbhIkUVcuK43u3UXt9OnaGy5SoYXfp/cj8u9X8uSdL169f0TruW6t67n/Lmy6/3OrbWgCEj1KDxmzpx7Ij69uqmLyZMUeky5Ywu9T9J75L6PptiYmLUqN6f+1LXP/elIX8e86ZMl9Vq1epVK/XF2NG6d++egk+cNrrk/5cMLqnwgykZ5O29zugSks2FCQ2MLuGZcLijib+/v4KDg23B4KPZZY4cOeTm5qaoqCiFh4cbVaJhToYcV6EiRZKMv1b9da3duE2FChdRXFycbt2KkCwWZcyUyYAq8ZDVatWo4YNVuUo11WvQWFWrva6fNiTdTpnYTs9cu1qFNXdQfY2au9du3NUlnYq+lF1B56499nmtqr+iqauDdfDUFcUnWDVtzRHdvBMtv6oFJUlv1y2mr5cd1m9/ROruvVj1n75Tdcrk1UvemZP9PZnN1SuXVbDgK+ryfne5uLjKyyuLmrVopaDAAMXGxir03FkVKVLU6DLxp78e765c/l1+zVupymvV5eTkpLz5fFW9Ri0FBwUYXarpZMmaVVt27lPjJn6yWCy6feuWYmNjlSVrVrv1IiIiNHzIAPUfPJzQKYX90/HuURERERo6eIAGso0M8U/bqVr1Glq/eZsKFymquLg4RUREyGKxKFMmT6PLNjWr1aqPhw9WpaqJn03bNm/UqyVLq4lfczk7O6tEqTKqU6+hVi5fYnSppnL1ymUVfPkVden2yL7U8n/HvFEfDdXKFcv0fveeBleKZ8qShh9phMNdVuzu7q6yZcvKarXKYrEkmR343HPPKTY2VkFBQXJ3d1eGDBkMqjRlJSQk6PSpk3Jzd9f8ubOVEJ+gylVfU8/e/eTpmVkeHh66ePGCWvk1Unx8vNp16KRXChU2umxT27BurX45H6ovJky2jXl4eOjXixfUplljxcfHq22Ht/TyK2ynZ21r4EUt2X5K8QlWLRj6v/HieXPIxTmdRnSopIpFculOVKzmbTqhr388LKtVSpfOonsxD+xeK8Fq1csvZJVnBlc9nyOTTlz8w7bs+q17ioiMUbG82XXx6u2Uenum8FLefJoyfZbd2NYtm1SocBGdPXNacXFxmjplko4EBSljpox6s2lzvfX2u1yyapC/Hu9q1HpDNWq9YVseExOjfXt2qU79hkaVaGoeHhklSfVrV9f169dUslQZNW7iZ7fOtxO+VOHCRVW/QSMjSjS1fzrePWriN39uo4ZsIyM8aTt5eGTUxQu/qHnTxHPx9h05Fzfaxj8/m8Z/k/jZlJAQLzd3d7t1nJyc9OuFC0aUZ1qP3Zc2/29f+qDHh8rp7a0A/0NGlAeYlsN8i7p48aIkqVixYvLx8VFQUJCio6Mfu+6xY8e0fv16xcbGpmCFxoqICNfLrxRSzVp19OOqdfp+/mJd+vVXfTR0oG2d53M/r32HgjV/8XJt3rhec7+f9Q+viOSUkJCg72dO09vvdZGHh4fdsty5n9fug0Gau2iZtmzcoPlzvjOoyrTrWsQ9xSckvTTd08NVu4+FacrqYOVvP0vvjN+gD5qUVO9mZSRJP+09pw+alFTxfDnknM5J79UvroLPZ5Wbq7MyZXCVJEX9JTyMvh8nD3fX5H9TJma1WjVl0gTt3rlDAwYNVeTduypTtpzatO2gjVt3avTnX+iHxQu0YN4co0s1pX863klSVFSUBvbpqfTp06tN+7cMqBAPrfp5kzZu3SWndE4a2O9D2/jvv/2mdWvXqMeHfQ2sDlLS491DD7dRr95sI0fwd9sp9/Mv6MDhI1q4ZLk2bVyvObM5FzdKQkKCvp81TZ3e/d9nU7XXa8n/wD5t37pZcXFxOnokSFs2rdf9+zEGV2tetn1p1w4NGJy4L+X09ja4KsCcHCIc9Pf314YNGxQSEiJJypkzp3Lnzq3Tp08nCQgDAwO1f/9+de/eXV5eXgZUa4xs2bJr1pyFatK0mdzc3eXtk0u9+vTX/r17FBWVeDMEZxcXObu4qHCRomrdroM2bUi71/U7usDDh3Tzjxtq3LRZkmUPt1OhIkXVqi3bKSVtD76keoN/1N7jvykuPkEBZ69q8k9BavZa4mXD3/wYoIVbT2rZyMY6u+A9vfxCVm0JvKhbkfdtoWCG9PYTrt3TOyvynnl+qEhpkZGR6t+3l9atW6PZcxeoQMGXVaFSZc2cPU9lypaTi4uLihYrrnbt39KmjeuNLteU/ul49+vFC3qvYxvFx8dp6ndzHxseIuW4ubkpx3M51at3f+3ft0d37iTOeF790wq9WrKkXn6lkMEVmtvjjncP/bRqhUqwjRzCP20nFxcXubi4qEiRYmrbrqM2rP/ZwErN7XGfTcVLlNTIz8Zq9owpql+rqhbN+14NGzdVJk8u/zZCZGSk+vfppXU/J92XAKQ8w8PBw4cP68yZM+rWrZs8PT114sQJSZK3t7d8fHx06tQpW0AYEBCg3bt3q1WrVsqZM6eRZae4c2fP6NsJX9n1YHwQGysnJyctX7pYQwbY33TgQWysPD3pg2aUHVu3qFqNWnJ3/99l7z8smKdhA+1/8Y99wHZKSY0q+urd+sXsxlxd0ikmNk6SlDt7Rs3bdFyvvDVb+drO1OBZu1Q8bw4FnbuqW5H39fuNuyr8Yjbbc3NmyaBsnu4K+fUP4dkLC7uk9m2aKyoySouWrLCdNO7YtlU/LrPvDxQbGys3NzcjyjS9xx3vJGnfnl16u30rVahcRROnzuJYZ5CjR4Lk17ieHjz4348YD2Jj5eLiIvc/L6/bvnWzGjRsYlSJ0N8f7x7atnWzGjRiGxnt77bTwvlzNai//bl4bGysMmfmuGeUHdu2qNrr9p9Nt2/fUj7f/Fq0fLU27zyg8d9M1rVrV1WoMD2MU1rYpUtq3/rPfWlp0mMe0p6HLePS4iOtMDQc9Pf316lTp9ShQwdJ0gsvvCB3d3fbDEJvb2/lypVLYWFh2rdvn/bv36+2bduaLhiUJM/MmbVsyWLNnztbcXFxunrlsiZ+84UaNn5T5StU0s4d27Rl0wYlJCToSHCQfli8QM1btja6bNM6eiRIJUuVsRsrUbqMdu3Ypq1/bqejwUFauniBmrVoZVCV5mOxWDS+S3VVL/GCJKl8IR91b1JS360/LklqUe0VLRvZRFkzucnDzUWfvVNV9x/Ea93BXyRJ87eEaFCbCnoxp6cyurvoi67VtftYmC5cod/gs3bn9m11ebeTXn21pKbO+E5ZsmSxLbPKqq++GKtDBw/IarXq6JFgLV40X82asy8Z4XHHu+PHjmpQ317q3X+QPuw7UM7ODtfi2DQKFHxZMTEx+nbCV3rwIFZXLv+ub74aryZNm8vFxVW3bkXowi/nVap0mSe/GJLFPx3vJLGNHMQ/badSpctox/at2rzxkXPxRfPVomUbAys2t6NHglTiL59NYZd+1bsdWuvcn72Lt2zaoL27d6oZ35lSlG1fKlFSU2cmPeYBMIZhZ+sHDhxQaGio7a7ECQkJcnJykq+vr86fP68TJ06oaNGi8vb21p07d7R9+3Z17tzZlMGgJOXM6a0Jk6dpyqRv9P2s6XJ1Ta836tZXrz79lT59eo37cqKmTZmozz7+SN4+udR/4FDVrlPP6LJN6/ffwpTjuefsxgoVLqLPv5yg6VMmafQnI+Tjk0t9BwxRLbZTilmzP1QDZ+7UxB41lTt7Jl0Lj9JnCw9oyfZTkqSJKwP1/HOZFDyrk1ydnbTvxO+qN+RH3X8QL0kas+igXNI5aduXrZQxg6t2Hw1T+9FcMpQcVv+0UlevXNbmzRu1ZfMmu2X7/YPUb8BgfT76Y127dk3Zs2VXtw96qkGjxgZVa26PO97Nmz1TcXFx+nrcGH09boxtvESp0powZWZKl2hqGTJ4aPK0Wfpy/BjVrl5FGTNlVP0GjfVe1w8kSZd//02SlOM5c55fOYInHe9+/3MbPcc2MtSTttMXX0/UlG8n6pNRw+Xjk0sDBg3TG3U5xzPK5cd8NhUt9qp69hmggX176vatCL34Uj59OWGK8vly9++UZNuXNm3Ulk1/2ZcOBxlUFQCL9dHrVFPIlStXtG3bNrVv316SbHcmftT58+cVHx+vggUTe4FFR0fbLn/5/7gbk/D/fg0kv/iU/2eJ/8Cn6USjS8AT3FzT58krwXCx8Xw2OTpn7oSdKjilnat7AEPdj+NzKTVI78Jnk6PL4MIHkyTl65t2e4P/8nV9o0t4Jgw5mnh6eqp48eK2Pz8aDD7MKn19fXX27FmtWbNGkuglBQAAAAAAkMoY3ReQnoNPZkg46OHhoTx58ujYsWOKj4+3W/bwf25wcLBCQ0NVvnx5u3EAAAAAAAAAz4Zh85C9vLyUJ08ehYSEJAkIAwMDtXPnTrVp08a0PQYBAAAAAACA5GZok4LHBYSBgYHavXu3ae9KDAAAAAAAAKQUw+5W/JCXl5ck6cyZM7p375527Nihjh07EgwCAAAAAAAAyczwcFBKDAidnJwUFBREMAgAAAAAAJBGcAsJx+cQ4aCUeAfjatWqceMRAAAAAAAAIIUY2nPwrwgGAQAAAAAAgJTjUOEgAAAAAAAAgJTjMJcVAwAAAAAAIG3hKlHHx8xBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgWtBx0fMwcBAAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACT4oYkAAAAAAAASBYW7kji8Jg5CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEnRcxAAAAAAAADJgpaDjo+ZgwAAAAAAAIBJEQ4CAAAAAAAAJkU4CAAAAAAAAJgUPQcBAAAAAACQLJycaDro6Jg5CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEnRcxAAAAAAAADJwkLLQYfHzEEAAAAAAADApAgHAQAAAAAAAJMiHAQAAAAAAABMinAQAAAAAAAAMCluSAIAAAAAAIBkYeGOJA6PmYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFD0HAQAAAAAAkCxoOej4mDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdFzEAAAAAAAAMnCQtNBh8fMQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKW5IAgAAAAAAgGTBDUkcHzMHAQAAAAAAAJMiHAQAAAAAAABMynSXFTs5MZ01NbBY2U6pwfXVvY0uAU+QrXxPo0vAU7i6f5LRJeAJ0nH+kCpYrVajS8ATcC6eOqR3Zg5JauDEpZoAnhHThYMAAAAAAABIGeTYjo+fhAAAAAAAAACTIhwEAAAAAAAATIpwEAAAAAAAADApeg4CAAAAAAAgWVhoOujwmDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAIBkYbGk3cd/ER4ertq1a+vQoUO2sU2bNqlJkyYqVaqUatSoocmTJyshIcG2fNWqVapdu7ZKlCghPz8/BQcH25bFx8dr3LhxqlSpkkqWLKlu3brp+vXr/6omwkEAAAAAAAAgmQUGBqpVq1a6dOmSbezEiRMaOHCgevfurYCAAM2aNUsrV67U3LlzJUmHDh3Sp59+qrFjx+rw4cNq3LixunXrpujoaEnStGnTtG/fPq1YsUJ79uyRm5ubhg8f/q/qIhwEAAAAAAAAktGqVavUv39/9enTx278999/V+vWrfX666/LyclJvr6+ql27tg4fPixJWr58uRo0aKDSpUvLxcVFnTp1UpYsWbR+/Xrb8s6dO8vHx0cZM2bUsGHDtHv3boWFhT11bYSDAAAAAAAAwL8UGxuryMhIu0dsbOxj161SpYq2bNmi+vXr243XqVNHQ4YMsf05JiZGO3fuVJEiRSRJoaGhKliwoN1z8ufPr9OnT+vu3bu6evWq3fLs2bMrc+bMOnPmzFO/D+enXhMAAAAAAAD4Fyz/tTlfKjBjxgxNnjzZbqxHjx7q2bNnknVz5MjxxNeLjIzUhx9+KDc3N3Xq1EmSFBUVJXd3d7v13NzcdO/ePUVFRUmSMmTIkGT5w2VPg3AQAAAAAAAA+Je6du2qt99+227M1dX1P73WL7/8ol69eilbtmyaP3++MmbMKElyd3dXTEyM3boxMTHKkiWLLTR82H/w0eUeHh5P/XdzWTEAAAAAAADwL7m6uipjxox2j/8SDu7atUstWrRQ1apVNXv2bGXOnNm2rECBAjp37pzd+qGhoSpQoIAyZ86snDlzKjQ01Lbsxo0bunXrVpJLkf8J4SAAAAAAAABggCNHjqh79+4aMmSIBg0aJGdn+4t8mzdvrrVr1+rgwYN68OCB5s6dq5s3b6p27dqSJD8/P02bNk1hYWGKjIzUmDFjVK5cOeXJk+epa+CyYgAAAAAAACSLNNxy8JmYPn264uLiNHr0aI0ePdo2Xrp0aX333XeqWLGiRo4cqVGjRunatWvKnz+/Zs2aJS8vL0lS9+7dFRcXp3bt2ikqKkrly5fXhAkT/lUNFqvVan2G78nhRcWa6u2mWub6V5l6WcWGcnTPVehldAl4Clf3TzK6BDyBqzMXW6QGJjutTZWcnPiGmBokJLAvpQbsT47PjelYkqQyn+0wuoRkEzD8daNLeCY40wUAAAAAAABMinAQAAAAAAAAMCkmuQIAAAAAACBZWGg66PCYOQgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJEQ4CAAAAAAAAJsUNSQAAAAAAAJAsuB+J42PmIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFz0EAAAAAAAAkCwtNBx0eMwcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKXoOAgAAAAAAIFnQctDxMXMQAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKG5IAAAAAAAAgWVi4I4nDY+YgAAAAAAAAYFKEgwAAAAAAAIBJEQ4CAAAAAAAAJkXPQQAAAAAAACQLWg46PmYOAgAAAAAAACaVKsLBhIQEo0sAAAAAAAAA0hyHv6x47969ioyMVNmyZZU1a1ZugQ0AAAAAAAA8Iw4dDgYEBCg0NFSNGjXS/fv3FRERoSxZshAQAgAAAAAApAJkOI7PYS8r9vf318mTJ9WpUydly5ZN6dKlU2RkpMLDw2W1Wo0uzxD+hw6qY9uWqlqhtGpXr6JxYz5VTEyM3To3blxXrWqVteanlQZVCUmKj49Xl3c6aOTwwUmWHTsarIplihtQFf4qcTt11KjhQ2xj27ZsVtsWTVWtYhk1qltTM6dNobVBMsueJaNOrB6pqqULSJImDWutG/u+sntEBkzSmindkzy3RvlXFBkwSXl8strGLBaLhnSpq3MbPtX1vV9q9/z+Kl88b4q9HzO5ffuWRg4fpFrVKqhm1fLq37uH/rhxXZK0b88utW/lp+qVSqttyze1Y/sWg6tFeHi4GtWrrcP+h2xjS35YpEb131DFsiXVqP4bWrJ4oYEVmteZM6f1fud3VK1yedWqXkXDhw5SRESE3To3blxXTc7xHMrtW7c0bMhAvVapvKpULKvePT/QjT+PgTDGP+1LS39YpMYN6qhSuVJq3KAOxzsHcub0aXV9721VrVhONV6rrGFDBioiItzosgBTcchwMCAgQKdPn1bHjh1tYzlz5pS7u7uioqJMGRBGhIfrw+5d1bxla+3af1iLl69U4GF/zZk907ZOQkKChg0eoFu3Iv7hlZASZk6fouCgQLsxq9Wq1atWqHvXdxUbG2tQZXjUrOlTdOSR7XTqZIhGDBukbj16acc+f02aOlM/r16lxQvmGVhl2lbx1XzaObeffPPksI31Gr1EOSr3sz1a95ulW3ejNeirFXbPzZktk777tIPSpbP/KBvSua5a1S2jel2/lfdrA7R6+xGtmPi+XF0cerJ8qjS434eKvndPq9Zu1pqN25UunZNGfzJCp0+FaECfnmreqo227j6kAYOH65OPhijwsL/RJZtWcFCg3mrXSmFhl2xju3Zu19RvJ2rcF1/rwOFgfT7uS33z1Xgd9j9oYKXmExMTox7dOuvVEiW0dece/fjTWt2+dUujPvrfD1ec4zmmvr176t69e/p54xZt2rpDTk7p9PHIj4wuy7T+aV/atXO7pk6epLHjv9J+/yB9Pu5LTfj6C453DiAmJkbd339Pr5Yoqe279mrlmp91+9YtjRg21OjSAFNxuHDQ399fwcHBtmDw0RAwR44ccnNzswWEZpIla1Zt3bVPjd/0k8Vi0e1bt3Q/NlZZsvxvtszM6VOUM6e3cnp7G1gp/A8d1Patm1Wj1ht24x+PGKpVK5ap6wc9DaoMjzr8mO10+fff1axFK1Wt9rqcnJyUN5+vqtespaDAAAMrTbvaNSqvuZ930qgpa/92nWxeHpoz5i31H79cp365ahu3WCyaM7qT5qzab7e+k5NFPdq9rr7jflTopetKSLDqm/nb1Lj7FNP9qJTcTp0M0YnjRzXik8+VydNTHh4eGjriE/X4sJ+2bt6oV0uW0pt+LeTs7KySpcqoTv2GWrH8B6PLNqU1q1dp6KD+6tGrj914teo1tGHLdhUuUlRxcXG6dStCFotFmTJ5GlSpOV29clkFC76iLu93l4uLq7y8sqhZi1Z2nz0zp0/Rc5zjOZSTISd0/NhRfTp6rDw9PeXhkVEjP/5Uvfv2N7o00/qnfala9Rpav3mb7XgXEcHxzlFcvXJZBV9+RV27dZeLa+J2a96ilYICDxtdGmAqDhcOuru7q2zZsrYvcX+9Nv25556Tt7e3zp07p3v37hlRomE8PDJKkurVqq6Wfo2VPXsONXnTT5J02P+gNm9YryHDRxhZoumF37ypT0cO0+ixX8rNzc1uWbfuH2ruwqV6pVBhg6rDQ4nbabg++8t2qln7DfUd8L9LwWNiYrR39y4VKsw2Sw5b959U4Uaj9OPmoL9d57MPmyjo5CUt2WAf0A7pXFc3Iu5q3k8H7MYLvPicsnhmkFcmd+1bNFBh28dqzZTuirn/QA/i4pPlfZhVyIljypvPV6tXLpdfozqqV6uqJn41Xtlz5FBCfILc3d3t1neyOOnixQsGVWtulSpX0doNW1SnXv0kyzw8MurihV9UvnRx9ejWRS1ateFzKoW9lDefpkyfpXTp0tnGtm7ZpEKFi0hKPMfbtGG9hnKO51BOHD+mfL75teLHZWpYt7ZqVquiL78YpxzZczz5yUgWT9qXHh7vKpR5VT0/6KLmLVtzvHMAL+XNp6kzvrPbblse2W5IGyyWtPtIKxwmHLx48aIkqVixYvLx8VFQUJCio6Mfu+6xY8e0fv16016a+dO6Tdq0bZfSpXPSgL4fKvzmTY36aKg+G/uFMmTwMLo800pISNDwoQPUrmMnFXz5lSTL+bXfMSQkJOijoQPV9m+200NRUVHq37uH3Nzc1LZDp5Qr0ESu3byr+Pi/7+f4Yq5satugnEZMWmM3XqV0frVpUFY9PluS5DlZPROPgR+0qaY2/WepQL2PFHzqktZO7S7PjG5J1sd/d+f2bZ07d1aXLv2qBUtWauHSVbpx/ZpGDR+s6jVq6dCBfdq+dbPi4uJ0NDhImzet1/2/9MlFysiePYecnf/+svrcz7+ggwFHtWjJj9q4YZ1dyxKkLKvVqimTJmj3zh0aMGiowm/e1MiPhmo053gO5/bt2zp39owu/XpRS1es0rIVP+n69WsaNnSQ0aVBSfelh3I//4IOHD6ihUuWa9PG9Zoze5aBVeKvrFarJk/8Rrt27tCgIcOMLgcwFYcIB/39/bVhwwaFhIRISuwvmDt3bp0+fTpJQBgYGKj9+/ere/fu8vLyMqBa47m5uSnHcznVq09/7d+3R8OHDlSbth1UuEhRo0sztTnfzVR61/Rq3baD0aXgH8z5bqZcXdOrddv2f7vOxQsX9Hb71oqPi9f02XPl4cEXMiO89WYFHTjyi46d/d02lj1LRn33SQe9M2ye7kYlDZruP4iTJI2euUGXrkQo5v4Djfh2rTJ5uKliiXwpVrsZuLq6SpL6DhgiDw8PZcuWXd169Nb+vbuVv2BBjfpsnGZNn6y6Natowbzv1aiJnzw9MxtcNR7HxcVFLi4uKlK0mNq276gN6342uiRTioyMVP++vbRu3RrNnrtA+QsU5BzPgT08Bg4cPEweHhmVLXt29ezVW3t379K9qCiDqzO3v+5LBQq+bFtmO94VKaa27Tpqw3qOd44iMjJS/Xr30rqf12rOvIV22w1A8jO8O/vhw4d15swZdevWTWFhYTpx4oSKFi0q7z9nWZ06dUqFChWSu7u7AgICtGfPHrVt21Y5c+Y0uPKUdfRIkD7+aJiWrlwtF5fEk5GHMycPHdivE8ePaeaMqZKkqMhIjR39ibZu2aRJU2YYVrPZrPt5tf64cV3VKpeVJMVEJwYXO3ds06599MxwFOt/XqM/blxX9crlJNlvp537/LV3zy4NG9RfTf1aqEfvvv842wbJ682aJTRh/ja7sVoVCylHlkxaMzXxzsVOf87lP7xsiL74frNmLNujBw/ilf6Rm49YLIm9CC1KQ/P+HUDefL6yJiTowYMHSp8+vSQpPiHx0u3bt24pn29+/fDj/2Z9Dh3Yh0uEHMyC+XN1/OgRjf9qgm3sQWysPDMT4qa0sLBL6vlBF3l759KiJSuUJUsWXblyWYEBh3X8L+d4n3OO5xDy+eZXwl+OgQkJibPhraLHrVEety9J0sL5c3X82FGN+/Ib27qxsbHKzPHOIYRduqTu3TrLxyeXFi/70a6vPoCUYei3Xn9/f7u7Er/wwguKjY1VSEiIihQpYgsIw8LCdOPGDQUGBpoyGJSkAgVfVkxMjCZ985V69emnGzduaMJX49WiVRsNGT7Sbt0GdWqoa7ceavxnP0KkjJVrNtj9eeTwxN51H3821ohy8DdWrFlv9+dRwxPvBjnqs891/OgRDejdU4OHj1STps2MKA9/yprZQ4Xy+WhvUKjd+JL1h7Vk/f/C9jw+WXVm/Scq2/JzXbqSeKOqpRsDNK6fn06ev6Krf9zRqO4NdevOPe08fDZF30NaV75CJeXO/bw+GzVMIz4Zo/sx9zVt8kRVe72mbt78Q927vKNZcxcpn29+7di2WXt279TchcuMLhuPKF26jCZ986U2bVyv2m/U1dEjwVq8cH6S8wokrzu3b6vLu51Urlx5jfxktJycEi/s8fHJpUOBx+zWrV+nht7nHM8hVKhYSc8//4JGDh+qT0d/rpj79/XtxG/0es1atj7hSFl/ty9JUqnSZTRpwlfavHGDar1RR8eOHtEPi+ZryDCOd0a7c/u2Or/zlsqWr6CPP7XfbgBSjmHh4IEDBxQaGmoLBhMSEuTk5CRfX1+dP3/ebgbhnTt3tH37dnXu3NmUwaAkZcjgocnTZ+nLcWNUq1oVZcyUUfUbNFbn9z8wujQgzfj+u5mKi4vTl2PH6MuxY2zjJUuV1qRp9OBKSS/lziZJunz99r9+bvdPf9Dw9+tr06xeyuaVUUEnL6nRB1MUc//Bsy7T1JxdXDR99gJN+GqsmjWuq9j7sapa7XX1GzhUmTw91avvAA3o00O3b0XoxZfy6auJU+Wbv4DRZeMRhYsU1ZffTNKUSRP0ycjh8smVWwMGD1OduklvXILks/qnlbp65bI2b96oLZs32S3b7//3N2yCsVxcXDR73gJ9OX6sGtWvo/ux91W9eg0NpE+aYZ60L33x9URN+XaiPhk1XD4+uTRg0DC9UbeeQdXioZ9WrdSVK5e1edMGbdm00W7ZwYBgg6rCs/bXG83C8VisD28LnIKuXLmibdu2qX37xJ5fVqs1yT+W8+fPKz4+XgULFpQkRUdHJ7nz4X8RFcs0/9Qg5f9V4r/gshnH91yFXkaXgKdwdf8ko0vAE7g6M5MhNTDgtBb/kpMTXxBTg4QE9qXUgP3J8bnRoUiSVPWrvUaXkGz29KtidAnPhCFnup6enipevLjtz48Ggw9P6nx9fXX27FmtWZPYr8jNjbtMAgAAAAAAAM+SIeGgh4eH8uTJo2PHjik+Pt5u2cOgMDg4WKGhoSpfvrzdOAAAAAAAAIBnw7BJrl5eXpJku/lIunTpbMsCAwO1e/du0958BAAAAAAAIC1gspfjM7SBjpeXl/LkyaOQkBDbDEKCQQAAAAAAACBlGN4e8+EMwjNnzujevXvasWOHOnbsSDAIAAAAAAAAJDOHuPWel5eXnn/+eUVGRhIMAgAAAAAAACnE8JmDD3l6eqpatWpciw4AAAAAAJBGEPM4PoeYOfgQwSAAAAAAAACQchwqHAQAAAAAAACQcggHAQAAAAAAAJMiHAQAAAAAAABMymFuSAIAAAAAAIC0hftLOD5mDgIAAAAAAAAmRTgIAAAAAAAAmBThIAAAAAAAAGBS9BwEAAAAAABAsqDloONj5iAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRc9BAAAAAAAAJAsLTQcdHjMHAQAAAAAAAJMiHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCkuCEJAAAAAAAAkgX3I3F8zBwEAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApOg5CAAAAAAAgGThRNNBh8fMQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKnoMAAAAAAABIFrQcdHzMHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCk6DkIAAAAAACAZGGh6aDDY+YgAAAAAAAAYFKEgwAAAAAAAIBJEQ4CAAAAAAAAJkU4CAAAAAAAAJgUNyQBAAAAAABAsnDifiQOj5mDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQ9BwEAAAAAAJAsLBaaDjo6Zg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUvQcBAAAAAAAQLKg5aDjY+YgAAAAAAAAYFKmmzl4/0GC0SXgKbik46eF1MBqdAF4oqv7JxldAp5Cvq5LjS4BTxA2u7XRJeAp3ImOM7oEPIFnBtN9/UiV7sfxnSk1SO/CXB/Hx/dapA4cTQAAAAAAAACTIhwEAAAAAAAATIp5/QAAAAAAAEgWFi6vdnjMHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCk6DkIAAAAAACAZOFEy0GHx8xBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgWFgtNBx0dMwcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKXoOAgAAAAAAIFnQctDxMXMQAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKG5IAAAAAAAAgWThxRxKHx8xBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgWtBx0fMwcBAAAAAAAAEyKcBAAAAAAAABIAeHh4apdu7YOHTpkGzt69KhatGihkiVLqkaNGlq+fLndc1atWqXatWurRIkS8vPzU3BwsG1ZfHy8xo0bp0qVKqlkyZLq1q2brl+//q9qIhwEAAAAAAAAkllgYKBatWqlS5cu2cZu376tLl266M0339Thw4c1evRoff755zp27Jgk6dChQ/r00081duxYHT58WI0bN1a3bt0UHR0tSZo2bZr27dunFStWaM+ePXJzc9Pw4cP/VV2EgwAAAAAAAEgWFoslzT7+jVWrVql///7q06eP3fjmzZvl5eWldu3aydnZWRUrVlSjRo20aNEiSdLy5cvVoEEDlS5dWi4uLurUqZOyZMmi9evX25Z37txZPj4+ypgxo4YNG6bdu3crLCzsqWsjHAQAAAAAAAD+pdjYWEVGRto9YmNjH7tulSpVtGXLFtWvX99u/Ny5cypYsKDdWP78+XX69GlJUmho6N8uv3v3rq5evWq3PHv27MqcObPOnDnz1O+DcBAAAAAAAAD4l2bMmKHSpUvbPWbMmPHYdXPkyCFnZ+ck41FRUXJ3d7cbc3Nz07179564PCoqSpKUIUOGJMsfLnsaSasCAAAAAAAA8I+6du2qt99+227M1dX1X72Gu7u77t69azcWExMjDw8P2/KYmJgky7NkyWILDR/2H3zc858GMwcBAAAAAACAf8nV1VUZM2a0e/zbcLBgwYI6d+6c3VhoaKgKFCggSSpQoMDfLs+cObNy5syp0NBQ27IbN27o1q1bSS5F/ieEgwAAAAAAAEgWFkvafTwLtWvX1h9//KG5c+fqwYMHOnjwoNauXatmzZpJkpo3b661a9fq4MGDevDggebOnaubN2+qdu3akiQ/Pz9NmzZNYWFhioyM1JgxY1SuXDnlyZPnqWvgsmIAAAAAAADAAFmyZNH333+v0aNHa9KkScqaNauGDx+uChUqSJIqVqyokSNHatSoUbp27Zry58+vWbNmycvLS5LUvXt3xcXFqV27doqKilL58uU1YcKEf1WDxWq1Wp/x+3Jo4VHxRpeAp+CS7hlF8EhWpjp4pFLmOsKnXvm6LjW6BDxB2OzWRpeAp3DnXpzRJeAJPDMwNyE1uP8gwegS8BTSu3AhoKPL4ML3WklqMTfI6BKSzfJOpYwu4ZngaAIAAAAAAACYFD/dAQAAAAAAIFk4PavmfEg2zBwEAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApOg5CAAAAAAAgGRBx0HHx8xBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATCpVhIMJCQlGlwAAAAAAAACkOQ5/Q5K9e/cqMjJSZcuWVdasWWWx0MoSAAAAAAAgNSDHcXwOPXMwICBAoaGhKlu2rO7fv6+IiAhZrVajywIAAAAAAADSBIcNB/39/XXy5El16tRJ2bJlU7p06RQZGanw8HACQgAAAAAAAOAZcMhwMCAgQKdPn1bHjh1tYzlz5pS7u7uioqJMGxBevPCLen/QWbVfK68369fQ3O+m2/oxhp49ox5d31bNKmVUv1ZVTfxqnOLi4gyu2Jw2b1yv8qWKqmqF0rbHR0MHSpK2bd2sti2bqlqlMmpUr6ZmTp9CT00DbN64XhVKFdVrFUrbHiP+3Eb79uxSu5Z+qlaxtNq2eFM7tm0xuFrzun37lkYOH6Ra1SqoZtXy6t+7h/64cV2SNHb0KFUuW1zVKpa2PVb9uMzgitO2bJnS6/AXDVX5leeSLMuZ2U2nvm2qNlXyPva51Yt46/rcVnohu4dtzMli0ajWJXTq26b6dUZzLexdVTkzuyVb/WZ25vRpvf/eO6pWqbxqVaui4UMGKSIiQpJ0/NhRdWjTUpXKllKDOjW1asWPBldrPrciwtXWr56CA/1tY1+N/US1KpdU3WplbY81q5bblm/8ebXa+tVTndfKqkvHljpx7IgBlZsP+5Lj27h+rV6vVNruUaVscVUt96okaf+eXerY2k81KpdR+5ZNtXP7VoMrNrfw8HA1rveGAvwP2cb27N6l1s2bqnK5Umrp10Tbt3IuDqQEhwsH/f39FRwcbAsGHw0Bc+TIITc3N1tAaCb37kWpT/fOyunjozWbdmjadwu0dfNGzZk1TbciItTz/XdUtnxFbdpxQLPnL9G+PTu1dPF8o8s2pZMhJ1S/QWPtORhoe3w6ZrxOnQzRiGGD1K17L+3Y669JU2bq59WrtHjBPKNLNp2TISdUr0Fj7T4YaHt8Mma8Tp8KUf/ePdWidRtt23NIA4YM18cfDVHgYf8nvyieucH9PlT0vXtatXaz1mzcrnTpnDT6kxGSErfhkI8+0a4DgbZH0+YtDa447SpXILs2jqitfDkzJVlmsUgzulVStkyuj33uc5ndNKVLeaVzsj/l6NekiF4v6q2aIzep6Ic/KTo2XhPeLZcs9ZtZTEyMenTrrFdLlNDWXXv04+q1un3rlkYNH6I7t2+rZ7cuati4iXYf8NfIT0brq/Gf68TxY0aXbRrHjwbpg3fb6fffwuzGz5w8of5DRmnjrsO2R+OmLSRJwYH+mvjVGA0ZOVrrtu9XrboNNbR/T8XERBvxFkyDfSl1qFu/kXbsD7Q9lv60Xl5eWTR05Kc6feqkBvbtqWYt22rzroPqP3iYPhkxRIEBnOcZ4UhQkDq1a62wsEu2sVMnQ9S3Vw+1bN1Wu/b7a/CwjzRi2GC78BCpk5Ml7T7SCocLB93d3VW2bFlbKPjXxpXPPfecvL29de7cOd27d8+IEg1xNDhI4RHh6j94uNzdM8gnV251ererVv64ROt//kkvvPiS3nqni5xdXOSTK7cmTv1ONWvXNbpsUzoZclyFihRJMn758u9q1ryVqlZ7XU5OTsqbz1fVa9RSUFCAAVWa299toy2bNqpEyVJ606+FnJ2dVbJUGdWt31A/LvvBgCrN7dTJEJ04flQjPvlcmTw95eHhoaEjPlGPD/spNjZW58+dVaHCSbchnr3WVfJqZrdKGv3j47/kDnizqC6H39PvN5N+Jlss0oz3K2rBrl+SLOtQLZ8mrTuly+H3dDcmTkMXBqlW8Vx6MYdHknXx3129clkFX35FXbp1l4uLq7y8sqhZy1YKCgzQ1i2bldnLS63atJOzs7PKla+geg0aaekPi4wu2xQ2/rxan340SO9162U3Hhsbq1/On9PLhR5/jFu3eoVq1K6nYq+WkrOzi1q27ajMXl7avmVjSpRtWuxLqY/VatXHwwerUtVqqtegsbZt3qhXS5ZWE7/mcnZ2VolSZVSnXkOtXL7E6FJNZ83qVRoyqL+69+ptN75500aVLFVKfs0Tz8VLlS6jeg0aaflSthGQ3BwmHLx48aIkqVixYvLx8VFQUJCiox//C+ixY8e0fv16xcbGpmCFxkpISJCLi4ucnV1sYxYni8Jv3pT/gf3K55tf40aPUoPaVdW8cR1tWr9Wz+X0NrBic0pISNDpUye1d88uNaxbQ/VrV9foT0bozp3bqlnrDfUdMNi2bkxMjPbu2aVChQobWLH5JCQk6Mypk9q3Z5ca1a2hBo9so4SEBLm5u9utb3Fy0q8XLxhUrXmFnDimvPl8tXrlcvk1qqN6tapq4lfjlT1HDp07c1pxcXGaOe1b1a1RRc0a19W8ObO4RD+ZbD9+RaX7r9VPhy4lWVal0HPyK/+iBsx7/I8c/ZsU1Y0797Vot304mMndRbmzeehk2C3b2I07Mbp1L1ZFXvB6luWb3kt582nK9FlKly6dbWzr5k0qVLiIfjkfqvwFCtqtn8/XV2fPnEnpMk2pbIXKWrxyg2rUrmc3fv7cGcXFxen7mZP1Zt3X1K5ZAy2eN9t2jLvwy3nl8y1g95yX8vrq/Dm2W3JiX0p9Nq5bq1/Oh+rDfoMkSQkJ8UnO85ycnPTrBc7zUlqlylW0dsNm1alX3248IT5e7o/ZRhcuJP2REcCz5RDhoL+/vzZs2KCQkBBJif0Fc+fOrdOnTycJCAMDA7V//351795dXl5eBlRrjOIlSip9+vSa9u03iomO1pXLv2vR/DmSpARrgtatWaXCRYpp9frt+vzLifpp5TL9sHCusUWbUEREuF5+pZBq1qqjH1et0/fzF+vSr7/aeg4+FBUVpf69e8jNzU1tO3QypliTenQbLV+1TrPnL1bYr79qxNCBql6jlg4d2KftWzcrLi5OR4ODtGXjet2PiTG6bNO5c/u2zp07q0uXftWCJSu1cOkq3bh+TaOGD1Zk5F2VKlNOrdq018+bduiT0eO1bPFC2zERz9b12zGKT0ja5zd7pvT69r3y6jp9v6LuJ+1xW+nlHGpR6SX1m5P0cq1Mbs6SpHt/eV70/Th5uLkkWR/PhtVq1ZRJE7R71w4NGDxUUVFRcnfPYLeOm5u7ok10ZYaRsmXPLmdn5yTjkZF3VaJUWTVr1U4//rxNwz8ZqxXLFmnpormSpOh7UUkCjvRubmy3FMS+5PgSEhL0/axp6vRuF3l4JM5Ir/Z6Lfk/ep53JEhbNq3X/fuc56W07NlzPPb493rNWjqwf5+2btmkuLg4HQkK0qYN69hGQAowPBw8fPiwzpw5o27dusnT01MnTpyQJHl7e8vHx0enTp2yBYQBAQHavXu3WrVqpZw5cxpZdorLlMlTX387QyEnjqlJ/RoaPriv6jVoLElKiE9Q4aLF1ejNZnJ2cVGBgq+oeat22rZlk8FVm0+2bNk1a85CNWnaTG7u7vL2yaVeffpr/949ioqKkiRdvHhBb3dorfj4eE3/bq7thAUpI1u27Jo5Z6EaP7KNev65jfIXKKiPR4/TzGmTVbdGFS2Y970aNvFTJs/MRpdtOq6uif3r+g4YIg8PD2XLll3devTW/r27VezVEpo2a65KlSknZxcXFSlWXK3bddSWzRsMrtpcpr1fUTO3nNXRixFJlmXLlF5TulRQt+kHdDcmaXD4MEx0d7X/YuCe3lmRMQ+Sp2CTi4yMVP8+vbTu5zWaPXeBChR8We7u7kn61MXERCsDn0uGKlu+kiZM+14lSpWVs7OLChUppuat22vHn5cNu7m5J/nR6n5MjDJkYLulBPal1CHw8CHd/OOGGjdtZhsrXqKkRn42VrNnTFH9WlW1aN73ati4qTJ5ehpYKR5VomQpffb5eM2YOlm1qlXWvLmz1fhNP3lyLp7qWSyWNPtIK5LG9SnI39/f7q7EL7zwgmJjYxUSEqIiRYrI2zvxstiwsDDduHFDgYGBatu2remCQUl68CBW8fHxmjxjju0f4MrlS5Q3n69eKVxEQX9ppJuQkCCZ8I7ORjt39ow2rv9ZPT7sa9tOD2Jj5eTkJBcXF+3ds0vDBvdXU78W6vFh38f+Yobk9U/bKCYmWvl882vJijW29YcM6PPY/oRIXnnz+cqakKAHDx4offr0kqT4hHhJ0s7tWxUTEyO/5q1s68c+iFX69NzpNqXkzpZBlV5+TqXzZdOAJkUlJV4qPP6tMmpU7gWtOnhJ2T3dtHxAdUmS05/dmveMrqdv1oZo4s+JvQZfeT6zTv9+W1LijUuyZkyvU7/dNuQ9pWVhly6p5wdd5O2dS4uWrlCWLFkkSfkLFNDB/fvs1v3l/Hnlz1/gcS+DFLJn5zZFhN9UY7//3WTpQewDuf55jMvrW0AXfgm1e87FC+dVoVLVFK3TjNiXUo8d27ao2uu17GZ03r59S/l882vR8tW2sWGD+qpQ4aJGlIjHuH37lnzz59fyVWttY4P69VHhImwjILkZNnPwwIEDOnPmjC0YfNhHxdfXV25ubnYzCCVp+/btatmypSmDQSkx5+v9QWetXb1SVqtVp0+GaO7sGWrVtqMaNvHT+dCzWjh3tuLj4xV67qx+XLpYdRs0Mrps0/HMnFnLlizW/LmzFRcXp6tXLmviN1+oYeM3deb0SQ3o01N9+w9W734DCQYN4pk5s5YvWawFj2yjSX9uoyu//66327fW2T972m3euF57du9U85ZtjC7bdMpXqKTcuZ/XZ6OG6d69KEWEh2va5Imq9npNuWfIoG++HCv/QwdktVp17Giwli5eID/uVpxifr95T7nfW6Z83VbYHr/djNLAeQFq+/VuLd9/US90Xm5bVnVY4qzOqsM2aOLPpyRJi/f8on6NiyhPdg9ldHPW6HaltPfUNV28HmnkW0tz7ty+rS7vdtKrJUpq6szvbGGGJNWoVVt/3PxDixbM04MHD3TY/6A2rFurJn5+BlYMq9Wqyd+MV6D/QVmtVp04dkQ/Ll2oxn6Jdyuu36iptm5ap6AAf8XFPdDyHxYoIvymqlavZXDlaRv7Uupy9EiQSpQqYzcWdulXvduhta138ZZNG7R39041a9naoCrxV5d+/VUd2rTSmdOJ22jThvXavWuHWrbmXBxIbharNeWnl125ckXbtm1T+/btJSWeBP11Oub58+cVHx+vggUTm/tGR0cnaU76X4RHxf+/X8MowYEBmvjVWIVduqgsWbOpVdsOatE68f9hyPGjmjzhS50PPaf0bm7ya9Fand7tmmqnubqkS511S1JggL+mTPpG50PPydU1vd6oW1+9+vTX4AF9tHf3Trm52f87LlmqtCZNnWlQtf8/qXVu6sNt9Msj26hnn/5Knz69VixbovlzvtOtWxF6MW8+9fiwr8qVr2h0yf9Zap5AfOP6dU34aqyCAg8r9n6sqlZ7Xf0GDlUmT0+t/HGpFi+Yq+vXrilb9uxq16GTmrdqa3TJ/1m+rkuNLuGp3JzfRo3HbNO+09eTLAv+qpHGrzqhH/Ymbez+QnYPHfm6sUr0XaOwPxJbLDins2hos+JqUeklZXRz1t5T19Xne3/9cfd+sr+P/yJsdur88rhg3hx9/cU4ubm7yyL7z9b9h4MUcuK4vhg7RqHnzipLlqzq/H43NX4z9QYad+4lvYw9NahWrqgmTPteJUuXkyStWblMyxbP143r15Q1Wza1bNdJTZv/79/g5g1rteD7mbpx/apeypdfvfoNUeGixY0q/1/xzJA6fxw12750/0HqvsnX65VKa/T4b1Spymt24yuXL9GCubN1+1aEXnwpnz7o1UdlU/F5XnoXw7uE/b+VLPqKZn0/T2XKlZckLV+6RHO/n6VbERF6KW8+9erTT+UrpN5tlMEl9X6vfZbaLzxqdAnJZmH7V40u4ZkwJByMiorS+fPnVbx40pOYR4PCn3/+WQkJCWrcuPFjA8T/IjWHg2aSmsNBM0nFuZNppOZw0ExSSzhoZqk1HDSb1BoOmklqDQfNJrWHg2aRFsLBtI5wMFGHRWk3HFzQLm2Eg4YcTTw8PJQnTx4dO3ZM8fH2Yd3DADA4OFihoaEqX7683TgAAAAAAACAZ8Ownxq8vLyUJ08ehYSEJAkIAwMDtXPnTrVp08a0PQYBAAAAAACA5GboPOTHBYSBgYHavXu3ae9KDAAAAAAAAPwfe/cdHkXZvn38TCVLICS0UNRX6V16RxBBpCNVqlgAKUrvVZEigiAgHUQRREEQ6b23NKQEgiSKgCAtIZqEkLL7/hFZXUGR35NkNpnvh2OOR2Zm97n2GDIzOfee604vhjf98PX1lSSdP39ecXFx2rt3r7p160YwCAAAAAAAAKQxw8NBKSUgdHV1VUhICMEgAAAAAABAJsEcEs7PKcJBSfLx8VHdunX5RwMAAAAAAACkE6ea+5xgEAAAAAAAAEg/ThUOAgAAAAAAAEg/TvNYMQAAAAAAADIXVx4SdXqMHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCk6DkIAAAAAACANOHiQtNBZ8fIQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKSYkAQAAAAAAQJpgOhLnx8hBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgTri50HXR2jBwEAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApOg5CAAAAAAAgDRBy0Hnx8hBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIqegwAAAAAAAEgTLjQddHqMHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAk2JCEgAAAAAAAKQJ5iNxfo8VDs6cOVN169aVzWazzzZz/79tNpu2b9+uUaNGpUmhAAAAAAAAAFLXY4WDVqtVFStW/Mft+/bt+1/rAQAAAAAAAJBOHqvnoMsjxoI+ajsAAAAAAAAA50HPQQAAAAAAAKQJVwaSOT1mKwYAAAAAAABMinAQAAAAAAAAMKnHeqw4Pj5e169f/8ftMTEx/3NBAAAAAAAAANLHY4WDb7zxhpKSkv51OwAAAAAAACBJtBx0fo8VDvr7+6dVHQAAAAAAAADSGT0HAQAAAAAAAJN6rJGDO3bsUO7cuWWz2eTyx7jQ+/9ts9l05coVtWzZMk0KBQAAAAAAAJC6HiscPHPmjAYNGvSP2/fv3/8/FwQAAAAAAAAgfTxWOGiz2f6n7QAAAAAAADAPF2YkcXqP1XPwUQeUAw4AAAAAAABkHExIAgAAAAAAAJgU4SAAAAAAAABgUqbrOciTz0Dq4cfJ+Xm48x1QRvDz4leMLgGPkOuFd40uAf/B7V3jjS4Bj5Cc7Py/L0Dy5P4hQ3Dll1tkEJxRnN9jhYOlSpVSSEiIbDbbA/0FbTabChUqlKrFAQAAAAAAAEg7jxUONm7c+F+3V6pU6X8qBgAAAAAAAED6YXQnAAAAAAAAYFKPNXIwMjJSiYmJ/7qPv7///1QQAAAAAAAAMoe/t6WD83mskYPz589XcnKykpKSHJb76xYsWJBWdQIAAAAAAABIZY81ctDLy0sFChT4x+3ZsmX7nwsCAAAAAAAAkD4ea+Tgo4aCMlQUAAAAAAAAyDiYkAQAAAAAAAAwqcd6rBgAAAAAAAD4r1x5yNTpMXIQAAAAAAAAMKnHGjlotVoVEhIim81m7y94/79tNpvi4uLSpEgAAAAAAAAAqe+xwsEhQ4b86/ZKlSr9T8UAAAAAAAAASD/0HAQAAAAAAECaoOeg83uscHDp0qWqUKGCbDabw/r7jxUfPXpU/fr1S9UCAQAAAAAAAKSNxwoH79y5o4oVK/7j9n379v2v9QAAAAAAAABIJ481W/H9SUj+r9sBAAAAAAAAOA96DgIAAAAAACBNMJDM+T3WyEEAAAAAAAAAmQfhIAAAAAAAAGBSjxUO/n2W4sfdDgAAAAAAAMB5PFbPwUaNGikkJOSh22w2m+rWrZsqRQEAAAAAACDjc6XloNN7rHCwTJkyaVUHAAAAAAAAgHRGz0EAAAAAAADApAgHAQAAAAAAAJMiHAQAAAAAAABM6rF6DgIAAAAAAAD/lQsTkjg9Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUvQcBAAAAAAAQJpwpemg02PkIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFz0EAAAAAAACkCUalOT+OEQAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFBOSAAAAAAAAIE24uBhdAR6FkYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACkodDQUHXu3FmVK1dW7dq19f777yshIUGSdPLkSbVr104VKlRQ/fr1tWbNGofXrl+/Xg0bNlT58uXVunVrnThxIlVrIxwEAAAAAABAmnB1ccm0y39ltVrVq1cvNWrUSAEBAVq7dq0OHTqkxYsXKzo6Wj179lSrVq0UGBioSZMmacqUKTp16pQk6fjx45o4caKmTp2qwMBAtWjRQr1799bdu3dT7xil2jsBAAAAAAAAcBAdHa2bN2/KarXKZrNJklxdXWWxWLRjxw75+vqqc+fOcnd3V40aNdS8eXOtXLlSkrRmzRo1bdpUlSpVkoeHh7p37y4/Pz9t2bIl1erLEOGg1Wo1ugQAAAAAAADALiEhQTExMQ7L/UeF/8rPz0/du3fXBx98oLJly6pu3bp6+umn1b17d124cEHFihVz2L9IkSIKCwuTJIWHh//r9tTg9OHgoUOHtGPHDt2+fduergIAAAAAAABGWrhwoSpVquSwLFy48IH9rFarvLy8NHbsWH3//ffatGmTIiIiNHv2bMXGxspisTjs7+Xlpbi4OEl65PbU4J5q75QGgoKCFB4erubNm+vevXuKioqSn5+fXB7juW4AAAAAAAAYIzNHOL169dJrr73msM7T0/OB/Xbu3Knt27dr27ZtkqSiRYuqb9++mjRpkpo3b67ff//dYf/4+Hh5e3tLkiwWi+Lj4x/Y7ufnl2qfw2nDwYCAAIWFhal79+6SpOvXrysmJkY2m005c+YkIAQAAAAAAIBhPD09HxoG/t21a9ceeNzY3d1dHh4eKlasmA4fPuywLTw8XEWLFpWUEiReuHDhge3PPffc/1j9n5zyseKgoCCFhYWpW7du9nX+/v6yWCyKjY1VZGSkKR8xDjt3Vm+93lUN6lRT04bP6aNpkx/4x3Xr5k01fqGONn233qAqsWPbFlWrWEZ1qleyL2NHDZMk7d61Q53av6y6NSureeMXtGjBJ/TUNADHKGOJjIxU88YNFRhw3L7u9KmT6tKxnWpUqaAmjepr/TdrDKzQvAKOH1O3Tu31XI1KevH52po2ZaLi4+M1eeJ41a5W0WGpUr6U+r71htElZ2q5c2TVmVXvqE75p+3ryhTy15aZ3XRj20hd/HaIPujbSG5uf97+nVjRV7d3jNbNbaPsS/H/l9vhfV1dXbT6/Q4a/Vq99PkgJnT+fJje6vG66taqpgb1amvMqOGKiopy2OfmzRt6oW4tffftOoOqxL/dP9x36uQJ1azyrEEVQvrz2lSneiU1rFdbH0yeaB9xs3vndr3StpXqVK+kpo3qa+H8udznGWzzpu9UvXIFh6XSs2VUuXwZo0sDUlXt2rV18+ZNLViwQMnJybp8+bLmz5+v5s2bq2HDhrp165aWL1+uxMREHTt2TBs3blSbNm0kSW3bttXGjRt17NgxJSYmavny5bp9+7YaNmyYavU53cjBgIAAnTx5Uj169JAk2Ww2+yjBPHny6MaNG4qNjZUk5cqVy7A605vVatWQd3qr62tvat6Sz3Tr5g29/dYb8vX10+s9e9v3GT96mKLvRD3i3ZCWzoaeUZOmLTR+4mSH9efOhmrc6OGaOu0j1apTVz9f/En9+/ZSVktWdXn1tX94N6QFjlHGcSIkWONGj9Dly5fs636Ljla/3j3Vu987atuug0KCAzXwnb4qUqy4ypYtZ2C15hIVGakB/XppxJjxata8lW7fvqW+vd7Q8qWLNGrsuxo19l37vkePHNLo4YM1aMgIAyvO3GqUeVKLR72swk/ktK/LlSOrtszqptlfHVWLIV+oQB4fbZrRVddu/65Zq48oe9YsKvZkbpXsMEuXrkc/9H2fzJtDnwxrroZVi+h0xPX0+jimEh8fr369e6h1m3aaM2+BYmNjNXbUCE0YO1Ifz10gKeUeb/SIobrDPZ6h/un+QUr5neW7b9dpxkO+vEf6iYqMVP++vTRyzHg1a/HHtannG/p06SLVrVdfY0cN19TpM1W7Tl1dvPiT3unTU1mzZlXXV183unTTatqshZo2a2H/+/Xr19W5QxsNGDzUwKqA1FekSBEtXLhQs2bN0pIlS5Q9e3a1aNFCffv2laenp5YtW6ZJkyZp9uzZypkzp8aMGaPq1atLkmrUqKHx48drwoQJun79uooUKaLFixfL19c31epzunDQYrGoSpUq9lDw748P582bVwkJCQoJCZHFYlHWrFkNqjR9/f7bb7p166ZsNpt91KSLi4uyeHnZ91m6aJ7y5vVXXv98RpUJSWdDT6vBiy89sP7q1V/Upm0H1an7vCTpmUKFVa9+A4WEBBE8pTOOUcbw3Yb1mj93tgYMGqrhQwfa1+/auUM5fH31SsfOkqSq1WqoSdPm+urLlYSD6cgvZ07t3HdY3t7ZZLPZFH3njhISEuSXM6fDflFRURozcqiGjBijwkWKGlRt5tb5pWc17vXnNXrBTq2Y0M6+vstLzyr88m1NX3lIknTp1ztqNuhz3X/4okLx/Lr9W9w/BoNFnsilvfPe0ML1AcpmefTjMvi/+fXaVRUrVkI93+orNzc3+fp6qk27Dg4j0hYt+ER5/fPJPx/3eEb6p/sHSXpv3GhdvPijevZ+W7NmfJDOleE+v5w5tWu/47XpXkKC/Pxy6trVX9SmXQc998d9XqFChfV8/QYKCQoiHHQSNptNo0cMVZ3n6qlZ85ZGl4NU5EpXOElSzZo1VbNmzYduK1u2rFavXv2Pr23ZsqVatky7nwunCQcvXryop59+WmXLltX169cVEhKiUqVKPTAjiySdOnVKW7ZsUYkSJUwTDubw9dUrnV/V7I+mac7MD5WcnKzn6tVXxy6vSpKCA49r1/at+nTl1+rUlhOpUaxWq8LOnZWXxaLPly+VNdmqWnWe09sDBuuFBi/qhQYv2veNj4/XoYP71bhJMwMrNh+OUcZRs1ZtNWnaXO7u7g7hYETEBRUtWsxh30KFi2j9urXpXaLpeXtnkyQ1aVhPN25cV4WKldWiZWuHfebMmq5SpcqoSdPmRpRoCrsCIrR652klJ1sdwsHKJQsq9Kcbmj24mZrXLqG4+AR9tuWEPvwiJSysXKKg7sYnasfs7ir5dF79/OsdTfp0n7Ye/UGS9Ovt31W648f6Lfaew6PKSF1PP1NInyxY7LBu187tKlmqtCQpMOCYtm/dopVfrVXbl/k5Msq/3T/4+OTQW/3ekb9/PgUFBhhdqundvzY1bvDntallq9ayZM2qFxo2su/3530eP1fOYtPGDYqICNfHc+cZXQpgOk7RczAgIEBbt25VaGiopJT+ggULFlRYWJju3r3rsG9wcLCOHDmivn37puoQSmdntVqVxSuLBg8fo71HgrVq7Qb99GOEFs+fq8jI25o4frTenTRNWbN6G12qqUVFRap4iZJ6oUEjrV2/Wcs+X6VLP//8QD+a2NhYDRnQT15eXurUtbsxxZoUxyjjyJ07j9zdH/wOKy429oEvjry8vHQ3Li69SsPfrN+0Xdt27Zerm6uGDe5vX//LlSvavPE79es/yMDqMr/rkTFKTn6wZ5Zfdou6Na6goHO/qGjbj/TKmK/0ZovK6t+hhqSUERrBYVfVe9p3KtR6huauOaovJ7ZX1VJPSJJi7ibot9h76fpZzM5ms+mT2bN0YN9eDR0+SpG3b2v82FGaNPVD7vEM9qj7B3+e3HE6327eru2798vNzVVDB/V32BYbG6NB/fsqSxYvde72qkEV4q+sVqsWLZivN3u+ZQ94AaQfw0cOBgYG6vz58+rdu7cuX76sM2fOqEyZMsr3x2MT586dU8mSJWWxWBQUFKSDBw+qU6dO8vf3N7jy9LVvzy7t271TX63fLEkqVLio3ujVRx9Nm6yzoafVvmMXlfjjG2YYJ1eu3Fr86Rf2v+ezWPTOwCHq3qWDYmNj5e3trYsXf9KwQe8oV67cWrBkuX16cqQPjlHG52Wx6Pfff3dYFx8fr6wcJ8N4eXnJy8tL7wwYolc7t9dvv0XLxyeHNnz7jZ6tUEHFS5Q0ukRTupeYrKBzv+jzLSckSacjrmv+N8fV5vnSmrX6iGauPuKw/+qdp9W+QVm9XK+UAs5eMaJkU4uJidH4sSN17myoli5foSJFi6nvW2+qY6euKlWaxvxG+y/3D3Au9mvTwCHq1qm9fouOlk+OHLr4048aOqi/cubKpUVLPyOIchKBAcd16+YNvdy6rdGlAKZk6MjBgIAAnTt3Tl27dpUkPfnkk7JYLPYRhPny5VOBAgV0+fJlHT58WEeOHDFlMChJ1399+LTXv0VH60RwoJYtmq8GdaqpQZ1quv7rNX04ZaIGv9PboGrN68IP5zVn1gyH2bQTExLk6uoqDw8PHTq4X692bq+atepozvzF8vHJYWC15sQxyviKFC2miPALDut+jAhXEfrZpauT34eodYvGSkz889qUmJAgDw8P+8jOPbt2qGkzWl0YJeziTWXxcHNY5+bmqvttfwa8UlP1Kj7jsD2Lh7vu3ktMpwpx3+XLl9SlY1vFxsRq5epvVLRYcf366zUFBwVq0cJ5qlOziurUrKJfr13TlEnv6Z2+vYwu2XQedf8A53Dy+xC1bu54bUq4f23KatGhA/vVrVN71axVW58sWCKfHNznOYtdO7arfoOGpmkbBjgbw8LBo0eP6vz58+rWrZsk2aeQL1y4sLy8vHTmzBlJso8g3LNnj9q3b2/KYFCSqteopdu3bmr50oVKTk7WL1cu69MlC9W522s6cPx77Tp43L7458uvoSPHasbs+UaXbTo+OXLo69Wr9PnypUpKStKv167q45kfqlmLVjofdlZDB76tQUNGaMDgYQ99XBJpj2OU8b3QoKFu3b6lL1YsV2JiogIDjmnL5o1q1bqN0aWZStFixRUfH685s2YoMTFB167+opkzpqnly23l4eGpO3ei9NOPEapYqbLRpZrWZ1tOqHQhfw3qWEuuri4qXSiv3nq5qlbtOCVJeiKvj2YNbKqn8/vJzc1V3ZpUUPUyT+qLbScNrtxcfouOVs83uuvZZyto3sIl8vPzkyTlz19Ax4NP6eCRQPuSL39+jRw9TrM/WWhw1ebzb/cPnp5M2OMs7l+bZs9MuTZdvfqLZs2Yplat2+rc2bMaPKCfBg0bqYFDhnOf52ROnAhWpUpVjC4DacTVxSXTLpmFIWfEa9euKSIiwj5i0GazydX1z5yycOHCioiI0A8//KBixYqpWLFiGjJkyEMnJzGLZwoX0fSP52nhvNn6YvkyZcuWTY2aNtebvfoYXRr+wt8/n2bNna9PZs/UssUL5OmZRS++1ETvDByiEUMHKikpSdM/mKzpH0y2v6ZCxUqaPW+RgVWbC8co4/P19dOCRcs0beokzZ87W35+OTVs5BhVqVrd6NJMJWtWb82dv1jTp01Ww3q1lS17NjVp2sJ+Xbr6S8pjqXnymvNLPWfww6VbevGdTzW5z4sa0qW27sYnatGGIM375rgkadT8nbJabdo99zXlyOalcxdvqtWwlfrxl0iDKzeXDd+u06/XrmrHjm3auWO7w7YjASEGVYW/+7f7BziPrFm9NXfBYk3/YLIa1P3z2tTjrT4aNri/kpKS9OGUSfpwyiT7aypUrKS5f5sUCOnvyuUryuuf1+gyANNysf11bHw6iY2NVUREhMqVK/fANpvNJpc/0tdNmzbJarWqRYsWDuv/F1Fxyf/zeyDtuTPXOZAq3N2cYt4pPEKyNd0vxXhMeRq+a3QJ+A9u7xpvdAl4BM53GYMr9+IZghvHyel5MUBVkvTeznCjS0gz4xoWMbqEVGHIb43e3t566qmndOrUKSUnO4Z19wPAEydOKDw8XNWqVXNYDwAAAAAAACB1GJZj+/r6SpJCQ0NVunRpubn92TA7ODhYBw4cMO3kIwAAAAAAAJkBY72cn6HPm/n6+uqpp55SaGiofQQhwSAAAAAAAACQPgx/Av7+CMLz588rLi5Oe/fuVbdu3QgGAQAAAAAAgDTmFJ3qfX199cQTTygmJoZgEAAAAAAAAEgnho8cvM/Hx0d169Zl4hEAAAAAAIBMgom1nZ9TjBy8j2AQAAAAAAAASD9OFQ4CAAAAAAAASD+EgwAAAAAAAIBJEQ4CAAAAAAAAJuU0E5IAAAAAAAAgc3ER80s4O0YOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFL0HAQAAAAAAECacKXloNNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRc9BAAAAAAAApAl6Djo/Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEkxIQkAAAAAAADShIsLM5I4O0YOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFL0HAQAAAAAAECacKXloNNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRc9BAAAAAAAApAkXeg46PUYOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFL0HAQAAAAAAECacKXpoNNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQTkgAAAAAAACBNuDIfidNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRc9BAAAAAAAApAkXeg46PUYOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFL0HAQAAAAAAECacBVNB50dIwcBAAAAAAAAkyIcBAAAAAAAAEzKdI8VZ3EnD80I4hOtRpeA/yCLBz9Pzi4hiZ+ljCDJynFydpG7JxhdAv6DnI0mGV0CHiFy+2ijS8B/YJPN6BLwH9g4TABSCb/ZAwAAAAAAACZlupGDAAAAAAAASB8uzEfi9Bg5CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEnRcxAAAAAAAABpwpWeg06PkYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFD0HAQAAAAAAkCZcXWg66OwYOQgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJ0XMQAAAAAAAAaYKWg86PkYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFJMSAIAAAAAAIA04cqMJE6PkYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFD0HAQAAAAAAkCZoOej8GDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdFzEAAAAAAAAGmCUWnOj2MEAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACbFhCQAAAAAAABIEy4uLkaXgEdg5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRc9BAAAAAAAApAk6Djo/Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUvQcBAAAAAAAQJpwdaHroLNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmFSGmJDEarXK1ZUcEwAAAAAAICNhOhLn5/Th4KFDhxQTE6MqVaooZ86ccmGWGwAAAAAAACBVOPVwvKCgIIWHh6tKlSq6d++eoqKiZLPZjC4LAAAAAAAAyBScNhwMCAjQ2bNn1b17d+XKlUtubm6KiYlRZGQkASEAAAAAAACQCpwyHAwKClJYWJi6detmX+fv7y+LxaLY2FjTBoTnz4fprR6vq26tampQr7bGjBquqKgoSdIP58+r15vdVataRb1Qt5amT5uipKQkgys2p4s//agBfXqo4XPV1KpJfS1fskBWq1WStParVWrX8iXVr1VJ7Vq+pDWrVxpcrTkFHD+mbp3aq071SmpYr7Y+mDxR8fHxkqTdO7frlbatVKd6JTVtVF8L58+1Hz+kr+joOxo/Zrga1K2uF+pU05AB/XTr5g1J0o5tm9X+5aZ6vlZltWnxkr5Zs9rgas3p/vnuxeeq6+UmL2j5koX2n5ed27eoY+vmalCnqjq0aqL1a78yuFrcd+5sqF5/tbNq16isBvVq64Mp7yshIcHoskwjd46sOrOit+o8+5R9XZlCebVleifd2DREF9f21we9G8jNNaWVThYPN3309ov6aW1/3dg0RPvndlfdCv/P/loXF2lk19q6sPpt3dg0RAc+6a5qpQqm++cyk8jISDVv3FCBAccf2Hbz5g3Vf66mNny7zoDK8FeRkZFq0fhFBf1xnN5/d7xqVqnosFQqV0p9er5hcKXgupT5ubhk3iWzcLpwMCAgQCdOnLAHg38NAfPkySMvLy97QGgm8fHx6te7h54tX1679h3U2m83KvrOHU0YO1JRUVHq1aO7qlavoX2HjmvFqq908MA+rfriM6PLNp24uFgN7NtD/vnz67vtezV/yQrt2rFNny6er4P792rR/NmaOGWG9hwO1ruTP9Tcj6crOPDBG0uknajISPXv20tt27+i/UcCtWrNOgUHBujTpYt0NvSMxo4arj5v99f+I4GaM3+xNm5Yr5UrlhtdtimNGNxfd+PitH7jDn23bY/c3Fw16b1xigj/Qe9PGKux707S3sNBGv/eFH00bbJOhAQZXbKppJzveso/f35t2L5H85as0O4d2/Tp4gWKCL+gKe+O0+gJ72vXwQCNeXeSZn04Rd+HBBtdtulZrVa907eXGjRspAOHA7Ry9VodPXJIy5ctNro0U6hR+gntm/uqChfMaV+Xy8eiLdM7aU/wRRVo+ZGe67tcjasX0dttq0qS3n2jnqqULKjqPZbIv/l0rdxxWmvfby9vLw9J0siuddThhdJqPGSl8rWYoQ0Hz+ubSe3l6eFmyGfM7E6EBOvVzh10+fKlB7ZZrVaNGj5Ed+5EGVAZ/ur7kBB17/yKw3EaM/5dHQkMsS8zZs1W9uzZNXjYCAMrBdclwDk43YQkFotFVapUkc1mk4uLywMTkOTNm1cJCQkKCQmRxWJR1qxZDao0ff167aqKFSuhnm/1lZubm3x9PdWmXQeNHTVMG79br//3/57WG2/2kiQVKPiE5i9axuQtBjh5IkSRUZEaMmKMPDw8ZbFkVfc3emnm9MnatOOA1m/eLW9vbyUlJSn6TpRcXFyULXt2o8s2Fb+cObVr/2F5e2eTzWZT9J07upeQID+/nLp29Re1addBz9V9XpJUqFBhPV+/gUKCgtT11dcNrtxczp0N1ZnTJ7V19yFly5ZNkjRq3Hu6dfOmLv38k5KTk2Sz2lK+QHKRXN3clMUzi8FVm8upEyGK+tv57tU3emrW9CkqVKRIyjGyWf/4ks9Frm5u8sziaXTZpvfbb9G6efOmrPZjI7m6uMrLy2JwZZlf5xfLatxrdTV64W6tGNfavr5Lo3IKvxKp6V8ekSRduh6tZsNW6f7346MW7VEWDzfdvZekrF4eyuljUXRMvBKTrXJ1dVG/NlXU5b31Cr+S8sX5zK+PaW/IRVM+ZZPWvtuwXvPnztaAQUM1fOjAB7YvnP+J/P3zyT9ffgOqw30px2mOBgwaohFDBz10n6ioKI0aMVTDRo5R4SJF07lC/BXXJcA5OE04ePHiRT399NMqW7asrl+/rpCQEJUqVUoWy4MnhVOnTmnLli0qUaKEacLBp58ppE8WOH57smvndpUsVVqhp0+rSJGiev+98dq3Z7csFotavtxGr7/Z06BqzctqtcrDw0Pu7h72dS6uLoq8fVu///6bfHxy6OeLP6lzuxZKTk5Wxy6vqniJUgZWbE7e3ilhU+MG9XTjxnVVqFhZLVu1liVrVr3QsJF9v/j4eB06uF+NmzQ3qlTTCj1zSs8UKqwN69bomzWrdfdunGrUrKP+g4epQMGCKlPuWb3ZvZPc3NyUnJys/oOGqVSZskaXbSrJDznfubq6KvL2bZUp+6xKl31WvV7rYj9G/QYOVanSHCOj+fr6qUu37vroww80c/o0JScnq179F9SlW3ejS8v0dgX+qNW7zijZatOKcX+ur1yigEJ/uqnZAxqree1iiotP1GdbT+rDVYclSVarTXfvJen1phU0Z2BjJSYl67XJG5SQmKziT+WSX3aLfLNl0eEFr+sp/xw6eeFXDZu3S4lJtMRIbTVr1VaTps3l7u7+QDgYGHBM27dt1qqvvlGbVtw3GOmvx+mfwsGPP5quUqXLqEkzjpXRuC4BzsEpHisOCAjQ1q1bFRoaKimlv2DBggUVFhamu3fvOuwbHBysI0eOqG/fvvL19TWgWuPZbDZ9MnuWDuzbq6HDRyk6Olobvl2vMmXLaevOvZo+a46+WfOVvvj8U6NLNZ1y5SsoS5Ysmj9npuLv3tW1q79o5R/H4d69e5KkggWf0L4jIVr2xdfauX2rVixfYmTJpvbt5u3avnu/3NxcNXRQf4dtsbExGtS/r7Jk8VLnbq8aVKF5/RYdrQsXftClSz9rxep1+uKr9bp547omjBmhhIQEFSjwhOYuWKqDx07oo9nztWj+XB07ctjosk3lwfPdVa38fFnKRhcXFShYUB/PX6K9R4L14cfztHTBXB0/yjEymtVqVZYsXhoxaqyOBn6vtd9u0o8REZr/yWyjS8v0rkfFKtn64Gg+v+xe6vbSswoKu6qiHebolXFr9WazCurfrprDfit3nFKORlPV44ON+nRUS9Uo/YRyZk/5Er3Py1XUcfxaFe0wRycu/KqN0zrKx5vR1Kktd+48cnd/cGxF5O3bGjdmlCZPna6sWb0NqAx/9U/H6b5frlzR5o3f6Z0BDw8Okb64LpnD/adCM+OSWRgeDgYGBur8+fPq3bu3fHx8dObMGUlSvnz5lD9/fp07d84eEAYFBenAgQPq0KGD/P39jSzbMDExMRoy6B1t3vydli5foaLFisvT00NlypZVq5fbyMPDQ8WLl9Arnbpox/ZtRpdrOtmz++ijOQsVeuaUWjaprzEjBqlx0xYp27KlPD7s7uEhdw8PlSxVRh06dtWOrZuNLNnUvLy8lCevv94ZOERHDh/Ub9HRklImWejepaOSk5O1aOln9pGGSD+enimPnw4aOlLe3t7KlSu3evcboCOHDujjGdPkmSWLqlavKXcPD9V+rp5efKmJ1n/DhBfpKXt2H82Ys1Bnz5xSqyYvaOxfzndLFsyVp2cWValWQ+4eHqpVp64avNREG7752uCqsWf3Tu3etV3tX+kkT09PFSlSVL1699XXq780ujTTupeYrKCwq/p820klJVt1+scbmv9tkNrUK/XAfknJVq3Ze1Z7T1xUm3oldS8xWZI06fODunT9N8UnJGnckr3KntVTNco8YcTHMR2bzabRI4epU+euKlW6jNHl4D/4dv03Kl+hgoqXKGl0KRDXJcBZGBoOBgQE6Ny5c+ratask6cknn5TFYrGPIMyXL58KFCigy5cv6/Dhwzpy5Ig6depk2mDw8uVL6tKxrWJjYrVy9TcqWqy4JKlQ4SIPzOaUnJxMrxkDJCYmKDk5WXMXfqrte49q6edfyc3NTc8UKqz133ytMcMdv6FMSEyQT44cBlVrTie/D1Hr5o2VmPjnz0xCQoI8PDxkyWrRoQP71a1Te9WsVVufLFjC8THIM4UKy2a1KjEx0b4u2ZryS/Cvv15T4t/Oee7u7vLw8BDST8r5LklzFn6qbXuPaMnnq+X6x/kuKjLygeuSu7u73DlGhrt27dpDjw0/P8YJ+/mWsvxt8hA3VxfdH4uwYuzL9slJ7svi4abI3+N14cptJSYlK4vHn6OkXFxc5Orioj/fAWnp11+vKTgoQAsXfKLaNSqrdo3K+vXaVU15/1293aeX0eXhIXbv3KGmzVsaXQb+wHUJcA6GhYNHjx7V+fPn7bMSW60pfVEKFy4sLy8vhxGEkrRnzx61b9/etMHgb9HR6vlGdz37bAXNW7hEfn5+9m0tW7VR+IUftHzZEiUnJ+vCD+f11Zcr1bR5CwMrNiebTRrQp4c2blgnm82msLOhWr50oTp06qYKFSvrwL7d2rVjq6xWq05+H6Kvv1yhl9t2MLpsUylarLji4+M1e+YMJSYm6OrVXzRrxjS1at1W586e1eAB/TRo2EgNHDL8Xx9JQdqqVr2mChZ8Qu9PGK24uFhFRUZq/tyPVff5F9SocVPt3LFVR48cks1mU0hQgLZt2ahGTZoZXbap2GzSwD49tekv57vPli5S+05dVafu89q9c5uO/XGMTgQHavuWTWrUmGNktJq1auvWzZtasmiBkpOTdeXyZS1ZOJ++Wwb6bOtJlS6UV4M6VJerq4tKP5NHb7WqrFU7T0uSjoVe0aBXaqj0M3nk5uqi7k3Kq1LxAvpy12n9Hpegr3aH6oM+DfSUfw55erjp/R7P605MvPaduGjsBzOJ/PkLKCDktA4dDbIv+fIX0Mgx4zVn3kKjy8Pf3LkTpZ9+jFDFypWNLgV/4LoEOAcXmwHDy65du6bdu3erS5cukmSfmfivIiIilJycrGLFikmS7t69+9DJSR5XXELGHE234rNP9dH0D+RlsTzwTfCRgBCdPnVSM2d8qPALP8jL4qV27TvqzZ5vZdhn4OMTM24T7RPBQfp4xlRdvnRRfjlzqUOnrmr3Ssq/9YP792rRvNm6evWK8uUvoG7d31SjDDzZRRYPwzsT/J/8GBGu6R9MVuiZM8qWPZuaNG2hHm/10bDB/XVw/74HZkerULGS5v5tQqCMIik5Y57zJOnmjRuaNWOqQoIDlXAvQXXqPq/Bw0Ypu4+PvvryC61ZvVK3b92Uf7786v56T73UNOP+LCVZM+Y570RwkGbP+ECXL12Ur/1811mStGb1Sn3z1Srd+uMYdXutR4YOcLN6Zp4vC44dPaJP5szSTz/9qOzZsqtJsxZ6q09feXhk/NmkczaaZHQJ/8ndPaP14sAVOnjykiSpSokCmvzWCyr9TB7dvZekRd8F64Mv/uzROaRjDb3ZvKJ8vLPodMQNjVq4W8Hnr0mSPD3cNObVOmr3fGnlymFRyPlrGjhnu85dvGXIZ3uUyO2jjS4hVZQvU1yLl32uKlWrPbCt8Yv19VaffmrZqvVDXpkx2JRx7x/+qkKZElq87DNV/uM4hZ45rS6vtNPRoO/l5eVlcHX/u8wyQjgzX5csDICUJH114hejS0gzHSoUNLqEVGFIOBgbG6uIiAiVK1fugW1/DQo3bdokq9WqFi1aPDRA/L/IqOGg2WTkcNBMMmo4aCYZORw0k4waDppJZgoHM7OMEg6aWWYJBzO7zBIOZnaZJRzMzAgHUxAOprhz544mT56s/fv3y2q1qkqVKpowYYLy5s2rkydP6v3331d4eLj8/PzUu3dvtWvXzv7a9evXa968ebp586YKFSqksWPHqkKFCqn2OQz5zd7b21tPPfWUTp06peTkZIdt9wPAEydOKDw8XNWqVXNYDwAAAAAAAGQkb7/9tuLi4rRz507t3btXbm5uGjt2rKKjo9WzZ0+1atVKgYGBmjRpkqZMmaJTp05Jko4fP66JEydq6tSpCgwMVIsWLdS7d2/75L2pwbBhP76+vnrqqacUGhr6QEAYHBysffv2qWPHjqbtMQgAAAAAAICM78yZMzp58qSmTp0qHx8fZcuWTRMnTtSQIUO0Y8cO+fr6qnPnznJ3d1eNGjXUvHlzrVy5UpK0Zs0aNW3aVJUqVZKHh4e6d+8uPz8/bdmyJdXqM/SZwIcFhMHBwTpw4ICpZyUGAAAAAACAc0tISFBMTIzD8vcZuCXp1KlTKlKkiL7++ms1bNhQtWvX1gcffKA8efLowoUL9vk27itSpIjCwsIkSeHh4f+6PTUY3kDH19dXknT+/HnFxcVp79696tatG8EgAAAAAABABpeZ28QtXLhQc+fOdVjXr18/vf322w7roqOjdf78eZUpU0br169XfHy8hg0bpuHDhyt37twPTMDr5eWluLg4SSnzdvzb9tRgeDgopQSErq6uCgkJIRgEAAAAAACA0+vVq5dee+01h3Weng/OtH1/3ejRo5UlSxZly5ZNAwYMUPv27dW6dWvFx8c77B8fHy9vb29JksVieeh2Pz+/VPscTjPVqI+Pj+rWrUswCAAAAAAAAKfn6empbNmyOSwPCweLFCkiq9WqxMRE+zqr1SpJKlmypC5cuOCwf3h4uIoWLSpJKlq06L9uTw1OEw5KmXuoKQAAAAAAAMynZs2aevLJJzVq1CjFxsYqMjJSM2fOVIMGDdSsWTPdunVLy5cvV2Jioo4dO6aNGzeqTZs2kqS2bdtq48aNOnbsmBITE7V8+XLdvn1bDRs2TLX6nCocBAAAAAAAQObhkomX/8rDw0MrVqyQm5ubGjVqpEaNGilfvnyaPHmy/Pz8tGzZMm3btk3VqlXTmDFjNGbMGFWvXl2SVKNGDY0fP14TJkxQ1apVtXnzZi1evNg+h0dqcLHZbLZUe7cMIC7BVB83w4pPtBpdAv6DLB58v+DskpI552UESVbOec4uq6dTtGnGI+RsNMnoEvAIkdtHG10C/gObuH/ICFweK5qAESweRlfgHNZ8f9XoEtJMu/IFjC4hVfCbPQAAAAAAAGBShIMAAAAAAACASfGMDAAAAAAAANIEk886P0YOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJMSEJAAAAAAAA0gSj0pwfxwgAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACToucgAAAAAAAA0oSLi4vRJeARGDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdFzEAAAAAAAAGmCjoPOj5GDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQ9BwEAAAAAAJAmXGg66PQYOQgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJEQ4CAAAAAAAAJsWEJAAAAAAAAEgTrmJGEmfHyEEAAAAAAADApAgHAQAAAAAAAJMiHAQAAAAAAABMip6DAAAAAAAASBMutBx0eowcBAAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAwKToOQgAAAAAAIA04SKaDjo7Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEkxIQkAAAAAAADShAvzkTg9Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUvQcBAAAAAAAQJpwFU0HnZ3pwsFkq83oEvAfuDKmNUPg58n5ebhxIc4IXDnpOb2EJKvRJeA/uL1tlNEl4BFyNppkdAn4DyK3jza6BPwHTPIAILXw2wgAAAAAAABgUoSDAAAAAAAAgEmZ7rFiAAAAAAAApA8egXd+jBwEAAAAAAAATIpwEAAAAAAAADApwkEAAAAAAADApAgHAQAAAAAAAJNiQhIAAAAAAACkCSYkcX6MHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCk6DkIAAAAAACANOEimg46O0YOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFL0HAQAAAAAAECacKXloNNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRc9BAAAAAAAApAkX0XTQ2TFyEAAAAAAAADApwkEAAAAAAADApAgHAQAAAAAAAJMiHAQAAAAAAABMiglJAAAAAAAAkCZcmI/E6TFyEAAAAAAAADApwkEAAAAAAADApAgHAQAAAAAAAJOi5yAAAAAAAADShItoOujsGDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdFzEAAAAAAAAGnClZaDTo+RgwAAAAAAAIBJEQ4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUkxIAgAAAAAAgDThImYkcXaMHAQAAAAAAABMinAQAAAAAAAAMCnCQQAAAAAAAMCk6DkIAAAAAACANOFCy0Gnx8hBAAAAAAAAwKQIBwEAAAAAAACTyhDhoNVqNboEAAAAAAAAINNx+p6Dhw4dUkxMjKpUqaKcOXPKhYfVAQAAAAAAMgRSHOfn1CMHg4KCFB4eripVqujevXuKioqSzWYzuiwAAAAAAAAgU3DacDAgIEBnz55V9+7dlStXLrm5uSkmJkaRkZEEhAAAAAAAAEAqcMpwMCgoSGFhYerWrZt9nb+/vywWi2JjY00bEO7YtkXVKpZRneqV7MvYUcMc9rl184ZefL62Nm5Yb1CV5rZt80bVq1HJYalVuZxqV3nWYb/TJ79XnarljSkSio6+o/Gjh6vBc9VVv3Y1DRnQT7du3nDY59TJE6r1t+OG9HX+fJje6vG66taqpgb1amvMqOGKioqSJE2aOEFVK5ZVzaoV7cs3a74yuGLzSk5OVs/Xu2nCmJH2dVMmTlCNSuVUp1ol+7Ju7dcGVmle0dF3NH7McDWoW10v1HE8502dNEG1qpRT3RqV7Mt6jlO6+6fz3fvvjXc4z9WsWlGVni2lPr3eMLrkTC13jqw6s6K36jz7lH1dmUJ5tWV6J93YNEQX1/bXB70byM015UG5LB5u+ujtF/XT2v66sWmI9s/trroV/p/9tU/5+2jtpPa6umGQrn03WF+/11b/L1+OdP9cZhMZGanmjRsqMOD4A9tu3ryh+s/V1IZv1xlQGf5u29YtqliulKpXrmBfRo0YanRZgKk4Xc/BgIAAnTx5Uj169JAk2Ww2e5/BPHny6MaNG4qNjZUk5cqVy7A6jXA29IyaNG2h8RMnP3S71WrVmJHDdOdOVDpXhvteatpcLzVtbv/7jevX9VqX9uo3YLCklH/PGzes08xpU5SQkGBUmaY3fFB/Zffx0fpNO+Tq5qZ3x47UpHfHaebcBSnH6Nt1mjFtMsfIQPHx8erXu4dat2mnOfMWKDY2VmNHjdCEsSP18dwFCj1zWmPGv6cWLV82ulRIWrzgE30fEqwCBQra150NPaPR495Ts5atjCsMkqQRg/84521MOee9N26kJr03TjPnLNDZ0DMaOfY9NWvRyugyTetR57sx496173v0yCGNHDZYg4eOMLDizK1G6Se0eERzFS6Y074ul49FW6Z30uw1AWoxfLUK5M6uTdM66trt3zXr6+N69416qlKyoKr3WKIbd2LVo3klrX2/vZ5uM0ux8Yn68t22Cg67pkLtZsvFRZo7qIkWD2+uFwd+YeAnzdxOhARr3OgRunz50gPbrFarRg0fwu9MTiT0zGk1bd5SEydNMboUpBFX5o5wek4XDlosFlWpUsUeCv59ApK8efMqISFBISEhslgsypo1q0GVpr+zoafV4MWX/nH74oXzlNffX/758qVjVfgnNptNE8aMUK3addW4aQtJ0sTxo/XzxZ/Uo3c/fTxjmsEVmtO5s6E6c/qktu05pGzZskmSRo9/T7du3pQkvTdutH6++KN69n5bs2Z8YGSppvbrtasqVqyEer7VV25ubvL19VSbdh00dtQwJSQkKPzCDypduozRZUJS4PFj2rNrh+o3eNG+7v4xKlm6tIGVQfrznLd195/nvFHjUs55CQkJirjwg0qW4jgZ6d/Od38VFRWlUSOGatiIMSpcpKhB1WZunV8sq3Gv1dXohbu1Ylxr+/oujcop/Eqkpn95RJJ06Xq0mg1bpfsPMo1atEdZPNx0916Ssnp5KKePRdEx8UpMtkqS6r/9mZKtNiUlW+Xv563sFk/duhOX7p/PLL7bsF7z587WgEFDNXzowAe2L5z/ifz988k/X34DqsPDhJ45rRcbNTa6DMDUnCYcvHjxop5++mmVLVtW169fV0hIiEqVKiWLxfLAvqdOndKWLVtUokQJ04SDVqtVYefOysti0efLl8qabFWtOs/p7QGD5eOTQ0EBx7Vj2xat+HKNOrRpYXS5kLR180b9GBGuD2fNta/r1fcd+fvnU3BggIGVmVvomVN6plBhffvNGn2zZrXu3o1TjZp1NGBIyi9hb/XjGDmDp58ppE8WLHZYt2vndpUsVVo/nA9TUlKS5n0yW9+HhChb9mxq9XJbvfraG3J1dcpuGZlW5O3bmjh+jKZ/PFerVnxmX3//GC34ZI5Onkg5Ri1ebqNu3TlG6e3+OW/DOsdzXv/Bw3Thj+O0aH7KcfLOlnKcur7KcUpP/3a++6uPZ05XqVJl1KRZcyFt7Ar8Uat3nVGy1aYV4/5cX7lEAYX+dFOzBzRW89rFFBefqM+2ntSHqw5LkqxWm+7eS9LrTStozsDGSkxK1muTNyghMVmSdO+P//10VEu1r19av0bGqPHglen++cyiZq3aatK0udzd3R8IBwMDjmn7ts1a9dU3atOKnyVnYLVade5sqCwWi5YvW6Jka7Lq1KmrAYOGyCcHj98D6cUp7vwCAgK0detWhYaGSkrpL1iwYEGFhYXp7t27DvsGBwfryJEj6tu3r3x9fQ2o1hhRUZEqXqKkXmjQSGvXb9ayz1fp0s8/a+yoYYq8fVvvjh+l96d8qKxZvY0uFUq5yC1bNF+vvdlT3t5/HhN/f0Z1Gu236GhduPCDLl/6WV98tU4rv16vmzeua8LolEe0OEbOx2az6ZPZs3Rg314NHT5KMb//rspVqqpjp67atmufJk35UF+uWqEVn31qdKmmYrVaNXbUMHXq1l3Fipdw2BYT87sqVa6qVzp30Zade/Xe5Gn6atUX+oJjlO7un/MuXfpZK1av0xdf/XHOGzNCMTG/q2LlqurQsYs2bd+r9yZN09ervtDKzzlORvn7+e6+X65c0eaN3+mdAYMMrC7zux4Vq2Trg33N/bJ7qdtLzyoo7KqKdpijV8at1ZvNKqh/u2oO+63ccUo5Gk1Vjw826tNRLVWj9BMO23tP36w8zT7UN/vOavvMLvLxzpKmn8escufOI3f3B8fARN6+rXFjRmny1On8zuREoiIjVaJkKTV4sZHWb9yiz79YrZ9/vkjPQSCdGR4OBgYG6vz58+rdu7d8fHx05swZSVK+fPmUP39+nTt3zh4QBgUF6cCBA+rQoYP8/f2NLDvd5cqVW4s//UItX24jL4tF+fIX0DsDh+jwwQMaPWKIOnTsymNBTiQ48Lhu37qpFi+3MboU/I2Hp6ckadCwkfL29lauXLnV++0BOnzogOLiYg2uDn8XExOjIYPe0ebN32np8hUqWqy4qtespUVLP1PlKlXl4eGhMmXLqXOXV7V92xajyzWVT5cskqdnFr3SqcsD26rXqKUFS5erUuWqcv/jGHXs3E07t281oFJz87x/zhv6l3NevwE6cuiAyj5bXvMXL1fFP45T6bLl9Ernbtq5g+NkhIed7+77dv03Kl+hgoqXKGlgheZ1LzFZQWFX9fm2k0pKtur0jzc0/9sgtalX6oH9kpKtWrP3rPaeuKg29RyPV3xCkuLiEzViwW55e3moXoWn0/FTmJvNZtPokcPUqXNXlaItiVPJlTu3Pv18pV5u3VYWi0X5CxTQwMFDdejgAcXGxhhdHmAahoaDAQEBOnfunLp27SpJevLJJ2WxWOwjCPPly6cCBQro8uXLOnz4sI4cOaJOnTqZLhiUpAs/nNecWTMcZmlOTEiQi4uLAgOOacmieapXu6rq1a6qX69d09TJ72lAv7cMrNjc9u7aqbr1G8hiMcdj7xlJoUKFZbNalZiYaF9ntaY87mPCSdCd2uXLl9SlY1vFxsRq5epv7L8o7929S2u/Xu2wb0JCgry8vIwo07S2bPpOIUEBqlerqurVqqptWzZr25bNqlerqvbt2fXA7NEJiQnKkoVjlN6eecg5L/mPc96+Pbu0bi3HyRn80/nuvt27dqhp85YGVYewn28pi4ebwzo3Vxfd74y+YuzLerttVYftWTzcFPl7vLw83XXys7dUuUQBh9e6uboq8jfHJ6SQdn799ZqCgwK0cMEnql2jsmrXqKxfr13VlPff1dt9ehldnqn9cD5Msz6a7vB7bkJCglxdXeXh4WlgZUhNLpl4ySwMCwePHj2q8+fPq1u3bpJSHk+SpMKFC8vLy8thBKEk7dmzR+3btzdlMChJPjly6OvVq/T58qVKSkrSr9eu6uOZH6pFq9YKOnlO+w4F2Jd8+fNrxKhxmjV3gdFlm9bJ70NUoWJlo8vAQ1SrXlMFn3hCE8ePVlxcrKIiIzVvzseq+/wLDo+Aw1i/RUer5xvd9eyzFTRv4RL5+fnZt9lk04wPp+r4saOy2Ww6+f0JrVr5udq07WBgxebzzXdbtP9okPYdDtC+wwF6qUlTvdSkqfYdDpDNZtNHH05VwB/H6NTJE1q9coVat2tvdNmmU616TRUs+ITen/DnOW/+3JRzniVrVs2cPlUBx/88Tl+tWqHWbTlO6enfzneSdOdOlH76MUIVK3FfYZTPtp5U6UJ5NahDdbm6uqj0M3n0VqvKWrXztCTpWOgVDXqlhko/k0duri7q3qS8KhUvoC93nVZ8QpLCfr6lST3rK5ePRd5eHprV/yVduBKp42evGPzJzCN//gIKCDmtQ0eD7Eu+/AU0csx4zZm30OjyTC1HDl+tXrVSy5ctUVJSkq5dvaqZMz5Ui1Yv20e/A0h7hkxIcu3aNUVERNhHDNpsNofG14ULF1ZERIR++OEHFStWTMWKFdOQIUMeOjmJWfj759OsufP1yeyZWrZ4gTw9s+jFl5ronYFDjC4ND/HLlcvKkzev0WXgIdw9PLRw6QrNnD5VrZu/pIR7CXqu3vMaPGzUo1+MdLPh23X69dpV7dixTTt3bHfYdiQgRIOHjtCUSe/q+vXryp0rt3r3eVtNmzMZk7N4/oWGGjR0hKZOek83rl9Xrty51at3PzVpxjFKb+4eHlqwdIVmzZiqNi1Sznl16qac87L7+GjgkBGaNvnP49TzrX5q3JTjlJ4edb775ZeUAClvXnN+Qe4Mfrh8Wy8OWKHJb72gIZ1q6u69JC36Lljz1gdJkj5ZFyhLFnd9M6m9fLyz6HTEDTUZslI/Xb0jSer5wUZN7d1AIZ/2ks1m074TF9VqxGolJlkN/FSAc/DPl09z5y/U7FkfafHC+fLMkkUvNW6qgYPpOQikJxebLf0fpIuNjVVERITKlSv3wDabzSYXl5TBmZs2bZLValWLFi0c1v8vfo/nIpwRJPN8Z4bgmgo/k0hb7q4co4yAc57zs3L7kCF4uHHOc3a5XppsdAn4DyK3jza6BPwH3Io7Py9DhmM5n2Phd4wuIc1UL+JrdAmpwpDHir29vfXUU0/p1KlTSk5Odth2PwA8ceKEwsPDVa1aNYf1AAAAAAAAyCCMbgxI08FHMqznoK+vr5566imFhoY+EBAGBwdr37596tixo2l7DAIAAAAAAABpzdDZih8WEAYHB+vAgQOmnZUYAAAAAAAASC+GPwHv6+srSTp//rzi4uK0d+9edevWjWAQAAAAAAAASGOGh4NSSkDo6uqqkJAQgkEAAAAAAIBMwiUzNefLpJwiHJQkHx8f1a1bl4lHAAAAAAAAgHRiaM/BvyMYBAAAAAAAANKPU4WDAAAAAAAAANIP4SAAAAAAAABgUk7TcxAAAAAAAACZCx3knB8jBwEAAAAAAACTIhwEAAAAAAAATIpwEAAAAAAAADApeg4CAAAAAAAgTdBy0PkxchAAAAAAAAAwKcJBAAAAAAAAIB0kJyera9euGjFihH3dyZMn1a5dO1WoUEH169fXmjVrHF6zfv16NWzYUOXLl1fr1q114sSJVK2JcBAAAAAAAABIB3PnzlVQUJD979HR0erZs6datWqlwMBATZo0SVOmTNGpU6ckScePH9fEiRM1depUBQYGqkWLFurdu7fu3r2bajURDgIAAAAAACBtuGTi5TEdPXpUO3bs0Isvvmhft2PHDvn6+qpz585yd3dXjRo11Lx5c61cuVKStGbNGjVt2lSVKlWSh4eHunfvLj8/P23ZsuXxC/gHhIMAAAAAAADAY0pISFBMTIzDkpCQ8NB9b9++rdGjR2vGjBmyWCz29RcuXFCxYsUc9i1SpIjCwsIkSeHh4f+6PTUQDgIAAAAAAACPaeHChapUqZLDsnDhwgf2s1qtGjp0qF577TWVKFHCYVtsbKxDWChJXl5eiouL+0/bU4N7qr0TAAAAAAAAYBK9evXSa6+95rDO09Pzgf0WLlwoT09Pde3a9YFtFotFv//+u8O6+Ph4eXt727fHx8c/sN3Pz+9/Ld+OcBAAAAAAAAB4TJ6eng8NA/9uw4YNunHjhipXrixJ9rBv165dGjZsmA4fPuywf3h4uIoWLSpJKlq0qC5cuPDA9ueeey41PoIkHisGAAAAAABAGnHJxH/+q23btikkJERBQUEKCgpSs2bN1KxZMwUFBalhw4a6deuWli9frsTERB07dkwbN25UmzZtJElt27bVxo0bdezYMSUmJmr58uW6ffu2GjZsmGrHiJGDAAAAAAAAgAH8/Py0bNkyTZo0SbNnz1bOnDk1ZswYVa9eXZJUo0YNjR8/XhMmTND169dVpEgRLV68WL6+vqlWg4vNZrOl2rtlAL/HW40uAf9Bsrn+WWZYri7/h7nbka7cXTlGGQHnPOdn5fYhQ/Bw45zn7HK9NNnoEvAfRG4fbXQJ+A+4FXd+XgzHkiQF/fSb0SWkmcrP+BhdQqrgsWIAAAAAAADApMixAQAAAAAAkCYY5er8GDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASdFzEAAAAAAAAGmCloPOj5GDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQ9BwEAAAAAAJA2aDro9Bg5CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmxYQkAAAAAAAASBMuzEji9Bg5CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgEnRcxAAAAAAAABpwoWWg06PkYMAAAAAAACASREOAgAAAAAAACZFOAgAAAAAAACYFD0HAQAAAAAAkCZoOej8GDkIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASZmu56CLC0+7ZwRe7uTWGcG9RKvRJeBROOVlCHfvJRtdAh7BO4vpbpkyJs55Tu/2tlFGl4D/IGer2UaXgP/gytd9jC4Bj+Dlzv0DMgb+pQIAAAAAACBt8OWd02N4FgAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFz0EAAAAAAACkCReaDjo9Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUvQcBAAAAAAAQJpwoeWg02PkIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFz0EAAAAAAACkCVoOOj9GDgIAAAAAAAAmRTgIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASTEhCQAAAAAAANIGM5I4PUYOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFL0HAQAAAAAAECacKHpoNNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRc9BAAAAAAAApAkXWg46PUYOAgAAAAAAACZFOAgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJMSEJAAAAAAAA0gTzkTg/Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUvQcBAAAAAAAQNqg6aDTY+QgAAAAAAAAYFKEgwAAAAAAAIBJEQ4CAAAAAAAAJkXPQQAAAAAAAKQJF5oOOj1GDgIAAAAAAAAmRTgIAAAAAAAAmBThIAAAAAAAAGBShIMAAAAAAACASTEhCQAAAAAAANKEC/OROL0MMXLQarUaXQIAAAAAAACQ6Tj9yMFDhw4pJiZGVapUUc6cOeVC5AwAAAAAAACkCqceORgUFKTw8HBVqVJF9+7dU1RUlGw2m9FlAQAAAAAAAJmC044cDAgIUFhYmLp37y5Jun79umJiYmSz2RhBCAAAAAAAkAGQ3jg/pwwHg4KCFBYWpm7dutnX+fv76+bNm4qNjZUkUweEycnJ6t2ju/IXKKh335/qsO3UyRPq9carOhp0yqDq8DCRkZHq1qmDxr/3vqpUrWZ0OaYXHX1HH304RYcP7pfNalOFSlU0YvQ4LV44T9s2b3TY9969eFWpVkNz5i8xqFrzOh8WppnTp+nc2VB5eHioes1aGjxshPz8/HT61ElNmzJJEeHh8svppzd79tbLbdoaXbJpREVFqvdrnTV8zLuqULmqJGnf7p36bOkCXf3linx8cqhJ81Z69c235Orqqq7tW+r6tasO73H37l317NtfXV/rYcRHMJWA48c09+OP9NOPEfLysqjBi43Uf9BQeXl5affO7Vq8cL5+uXJZPjlyqEWr1urRq49cXZ364ZJMKzIyUt07v6Jx705U5arV9P6747Vl04PXpWrVa2jeoqUGVWle588/5Lo0NOW6NGniBG1Y/43c3T3s+w8eOlxt2nUwsOLMLbePRftmtFPv2bt18PQvkqSP+9TTqy+WVmJSsn2/4UsOatm2UAXP66yn8mZ3eI9sFk+NXX5E09cE6ak82fVR73qqWTq/XOSig6evaOjig/r5+m/p+rnM4OJPP2rWh1N09sxpeWfzVsvW7dXt9R6aPmWitm/5+znvnipXra5Z8xYbVC1gDk535xcQEKATJ07Yg8G/PkacJ08eeXl5KTY2VpGRkUaVaLhFCz7RiZBgh3U2m00b1n+jvr3eUEJCgkGV4WFOhASrW6cOunz5ktGl4A8jBvfX3bg4rd+4Q99t2yM3N1dNem+cRo6ZoP1Hg+3LBzNmK1v27BoweLjRJZtOfHy8+vXuoWfLl9eu/Qe1dsNGRd+5owljRuq36Gi93bunmrVoqQNHAzT+vUmaMW2KzpzmS5H0cOr7EPV+rbN+uXLZvu78uVC9P26k3uz9trbuPaoPZ8/X1k0b9PWqzyVJK77eoB0HA+1L+07dVLRYCbXp0Nmoj2EaUZGR6t+3l9q2f0X7jwRq1Zp1Cg4M0KdLF+ls6BmNHTVcfd7ur/1HAjVn/mJt3LBeK1csN7psU/o+JETdO7/icL8wZvy7OhIYYl9mzJqt7Nmza/CwEQZWak4O16V9B7X22z+uS2NHSpJCz5zWmPHv6UhAiH0hGEw7NUrm174Z7VS4gK/D+krF/NV3zm7labvAvizbFpqyrc9Kh/Vzvv1e30fc1PyNJyVJX45pqqu3Y1So61IV6rZUv99N1OKBDdP7o2V6cXGxGti3p/zz59eG7Xs0b8kK7d6xTZ8uXqBho8dr9+Eg+zJ5+sfKlj273uFeHEhzThcOWiwWValSxR4K/n10YN68eZUvXz5duHBBcXFxRpRoqIDjx7Rn1w7Vb/Ciw/p3x43S+m++Vq8+bxtUGR7mu2/Xa+SwIerXf6DRpeAP586G6szpkxr33hRl9/GRt7e3Ro17T/36D3bY705UlMaNHqohw0arcJGiBlVrXr9eu6pixUuoZ+++8vDwlK+vn9q076CQ4CDt2rlDOXx91aFjZ7m7u6tqtepq3LS5vvpypdFlZ3pbN23Qe2OGq0efdxzWX7t6VS3btFetOvXk6uqqp58prDr1XtDJv32RJUkhQQH6etXnenfqDGXNmjW9Sjctv5w5tWv/YbVo1VouLi6KvnNH9xIS5OeXU9eu/qI27TroubrPy9XVVYUKFdbz9RsoJCjI6LJN57sN6zVy+BD1fWfAP+4TFRWlUSOGatjIMVyXDPDrtasqVqyEer71l+tSu5TrUkJCgsIv/KDSpcsYXaYpdH6hhJYPa6QJnx91WO/p7qYyT+dWyIUbj3yP58o9obdblVeXqVsUG58oSao/ZI0GLdiv+IRk+WT1VHaLh25F302Tz2Bmp06EKCoqUkNGjJHFklX5CxTQq2/01Pq1qx0GBt2JitK7o4dr4NBRKlS4iIEVA+bgNOHgxYsXJUlly5ZV/vz5FRISort3H34yPnXqlLZs2WK6EXKRt29r4vjRmjR1ury8vBy29e7bX8u/+EolSpYyqDo8TM1atbVp20691LiJ0aXgD6FnTumZQoW1Yd0atW7eSI0b1NHHM6Ypd548DvvN+Xi6SpYqo5eaNjeoUnN7+plC+mTBYrm5udnX7dqxXSVLldaPEeEqUrSYw/6FChfWD+fPp3eZplO1ei2t/narXnixscP6ei801NuDhtn/fi8+XkcPHVCxv12TkpOTNX3ye3r1jbf05FP/L11qhuTtnU2S1LhBPbVv3UK5c+dRy1at9ULDRho8bKR9v/j4eB06uF8lS5U2qlTTqlmrtjZu3aFG/3K/8PFH01WqdBk1acZ1yQgPvS7tTLku/XA+TElJSZr3yWy9ULeWWjZrpE+XLpbVajWw4sxrV8gllXrjM609eMFhfblCueXh5qpxXarr4hdv6tSirhrctpL+3onK1dVFc/o+r6mrAxVxNdq+/l5ispKSrfp0yIv68fM3VKmovyascAwg8b9Ltlrl4eHh8Ai+q6urIm/f1u+///kI97zZH6lEqdJq1KSZEWUitblk4iWTcIpwMCAgQFu3blVoaMqQb39/fxUsWFBhYWEPBITBwcE6cuSI+vbtK19fXwOqNYbVatWYUUPVuVt3FSte4oHt/vnyGVAVHiV3njxyd3fK1p6m9Vt0tC5c+EGXLv2sFavX6Yuv1uvmjeuaMObPR7R++eWKtm7aqL5vM+LTGdhsNn0ye5YO7N+roSNGKTY2VhaL44gzLy+L7ppwNHl6y5U79yPPaXGxsRo55B1l8cqiDp26OWzbuW2z7t6NU9tXeJzYCN9u3q7tu/fLzc1VQwf1d9gWGxujQf37KksWL3Xu9qpBFZpX7tz/fr/wy5Ur2rzxO70zYFA6VoV/Yr8u7durocNHKeb331W5SlV17NRV23bt06QpH+rLVSu04rNPjS41U7oeFadkq+2B9T5ZPXXg9BV98t1JFXl1mV6fvkN9mj+rAS9XdNivQ93iyubloU+++/6h79979m7laTtf3xy6oO1TW8snq2dafAzTKle+grJkyaL5c2Yq/u5dXbt6VSs/XyYppb+gJF395Yq2bf5Ob709wMBKAXMxPBwMDAzU+fPn1bt3b/n4+OjMmTOSpHz58il//vw6d+6cPSAMCgrSgQMH1KFDB/n7+xtZdrr7dMkiZfHMolc6dTW6FCBD8/RMucEbNHSkvL29lStXbvXuN0BHDh1QXFzKhEcbv/1Gz5avoGIlShpZKiTFxMRoyMB3tHnTd1q6fIWKFisui8Wi+HjHL47i4+8qq7e3QVXivksXf9Jbr3VWcnKyZi9Y9sAx2bh+rVq83FZZ/jb6HenDy8tLefL6652BQ3Tk8EH9Fp0yYubiTz+qe5eOSk5O1qKln9lHGsJ5fLv+G5WvUEHFuS4ZLiYmRkMGvaPNm/+8LlWvWUuLln6mylWqysPDQ2XKllPnLq9q+7YtRpdrKnu+v6zGo9br0JlflJRsVdAP1zV3w/dq85zjY/hvvFRaS7edUXxC8kPfJz4hWXH3kjRi6SF5Z/FQvWefSI/yTSN7dh/NmLNQZ8+cUqsmL2jsiEFq3LRFyrZsKRPGbNqwTuXKV1Cx4pzzgPRiaDgYEBCgc+fOqWvXlMDrySeflMVisY8gzJcvnwoUKKDLly/r8OHDOnLkiDp16mS6YFCSNm/aoOCgANWtVUV1a1XRti2btW3LZtWtVcXo0oAM5ZlChWWzWpWYmGhfl2xNuTm83+Zk7+6datyshRHl4S8uX7qkLq+0VWxMrFZ+9Y2KFisuSSpStKh+DA932PfHiAgVoQeXoY4eOqCer3ZUtZq1NGPOQmX3yeGwPfL2LZ0+eUKNmvBIZHo6+X2IWjdvrMTEP1uxJCQkyMPDQ5asFh06sF/dOrVXzVq19cmCJfLJkeNf3g1G2b1zh5o2b2l0GaZ3+fIlden4x3Vp9Z/Xpb27d2nt16sd9k1ISHigDRDSVvPqhfTGS459Hz093BR/L8n+97y+FtUoVUCr9oY57Ofl6aaTC7uqcrE/f890c3WRm6uLIn+/l7aFm0xiYoKSk5M0Z+Gn2rb3iJZ8vlqubm56plBheVkskqR9u3fqpSbciwPpybBw8OjRozp//rx9VuL7PTkKFy4sLy8vhxGEkrRnzx61b9/elMGgJK37bqsOHA3W/sOB2n84UC81aaqXmjTV/sOBRpcGZCjVqtdUwYJP6P0JoxUXF6uoyEjNn/ux6j7/gry9vXXnTpR++jFCFSpWNrpUU/stOlo93+iuZ8tX0LxFS+Tn52ffVr9BQ926fUsrV3ymxMREBQYc09bNG9WydWsDKza30NMnNXpof709aJj6Dhj60McjT588odx58qrAE08aUKF5FS1WXPHx8Zo9c4YSExN09eovmjVjmlq1bqtzZ89q8IB+GjRspAYOGU4bDCd1/7pUsTLXJSPZr0vPVtC8hY7XJZtsmvHhVB0/dlQ2m00nvz+hVSs/V5u2zFacnlxcpGk96thH+lUrkU99W5bXkm1n7PvUKFVA1yJjdfHX3xxeG5+QrLBLkZr0Wi3l8vGSt5eHZvWupwtX7+h42LV0/RyZnc0mDezTU5s2rJPNZlPY2VB9tnSR2v/xhFz0nTu6+NOPKl+xksGVIjW5ZOI/mYUhd4HXrl1TRESEfcSgzWaTq+ufOWXhwoUVERGhH374QcWKFVOxYsU0ZMgQWf74JgEA/q/cPTy0YOkKzZoxVW1avKSEewmqU/d5DR42SpJ09ZdfJEl58prziwhnseHbdfr12lXt2L5NO7dvd9h2JDBE8xct1YdTJ2v+3Nny88upYSNHq0rV6gZVixXLFispKUkfT5+ij6dPsa8vV6GSps9eICmlf1DuPHmNKtG0smb11twFizX9g8lqULe2smXPpiZNW6jHW300bHB/JSUl6cMpk/ThlEn211SoWElzFyw2sGr81S9XrkiS8nJdMpT9urRjm3bu+Nt1KSBEg4eO0JRJ7+r69evKnSu3evd5W02bM/IpPX139EcNW3xQH/d5XgVzZ9P1qDi9v/KYVu/9c8KyZ/L56OrtmIe+vuesnZr6Zh2FzO8im03ad/KyWo3boMQkJpZJTZ6enpr60RzNnvGBPp4+Vb45c6lL9zfUsnU7SdLVqynnPO7FgfTlYvvrfOHpJDY2VhERESpXrtwD22w2m1z+mFJq06ZNslqtatGihcP6/0XMvXT/uPg/cHfLPAl8ZnYvkZslZ+fhzs9SRhATn/TonWAo7yyMqssIUuFWEWmNW/EMIdfLc4wuAf/Bla/7GF0CHiGXN/cPknTh+t1H75RBFfXPHIPYDHms2NvbW0899ZROnTql5GTHRrD3A8ATJ04oPDxc1apVc1gPAAAAAAAAIHUY1nPQ19dXTz31lEJDQx8ICIODg7Vv3z517NjRtD0GAQAAAAAAgLRm6BhXX19fSVJoaKhKly4tNzc3BQcH68CBA6adlRgAAAAAACCz4EFQ52f4A/D3A8Lz588rLi5Oe/fuVbdu3QgGAQAAAAAAgDRm2GPFf+Xr66snnnhCMTExBIMAAAAAAABAOjF85OB9Pj4+qlu3LhOPAAAAAAAAAOnEacJBiRmJAQAAAAAAMhOSHufnFI8VAwAAAAAAAEh/hIMAAAAAAACASREOAgAAAAAAACblVD0HAQAAAAAAkInQdNDpMXIQAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyKCUkAAAAAAACQJlyYkcTpMXIQAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAk6LnIAAAAAAAANKECy0HnR4jBwEAAAAAAACTIhwEAAAAAAAATIpwEAAAAAAAADApeg4CAAAAAAAgTdBy0PkxchAAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACToucgAAAAAAAA0gZNB50eIwcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKcJBAAAAAAAAIA2FhYXptddeU9WqVVWrVi0NGzZMkZGRkqSTJ0+qXbt2qlChgurXr681a9Y4vHb9+vVq2LChypcvr9atW+vEiROpWhvhIAAAAAAAANKESyb+81/Fx8frzTffVIUKFXTo0CFt2rRJd+7c0ahRoxQdHa2ePXuqVatWCgwM1KRJkzRlyhSdOnVKknT8+HFNnDhRU6dOVWBgoFq0aKHevXvr7t27qXaMCAcBAAAAAACANHL16lWVKFFCffv2laenp/z8/NShQwcFBgZqx44d8vX1VefOneXu7q4aNWqoefPmWrlypSRpzZo1atq0qSpVqiQPDw91795dfn5+2rJlS6rVRzgIAAAAAAAAPKaEhATFxMQ4LAkJCQ/sV6hQIS1ZskRubm72ddu3b1fp0qV14cIFFStWzGH/IkWKKCwsTJIUHh7+r9tTA+EgAAAAAAAA8JgWLlyoSpUqOSwLFy7819fYbDbNnDlTe/fu1ejRoxUbGyuLxeKwj5eXl+Li4iTpkdtTg3uqvRMAAAAAAADwFy7/vTVfhtOrVy+99tprDus8PT3/cf+YmBiNHDlSoaGh+uKLL1S8eHFZLBb9/vvvDvvFx8fL29tbkmSxWBQfH//Adj8/v1T6FIwcBAAAAAAAAB6bp6ensmXL5rD8Uzh46dIltWnTRjExMVq7dq2KFy8uSSpWrJguXLjgsG94eLiKFi0qSSpatOi/bk8NhIMAAAAAAABAGomOjtarr76qihUraunSpcqZM6d9W8OGDXXr1i0tX75ciYmJOnbsmDZu3Kg2bdpIktq2bauNGzfq2LFjSkxM1PLly3X79m01bNgw1erjsWIAAAAAAAAgjaxbt05Xr17V1q1btW3bNodtJ06c0LJlyzRp0iTNnj1bOXPm1JgxY1S9enVJUo0aNTR+/HhNmDBB169fV5EiRbR48WL5+vqmWn0uNpvNlmrvlgHE3DPVx82w3N0ycVOCTOReotXoEvAIHu78LGUEMfFJRpeAR/DOwvepGUFm7mmUaXArniHkenmO0SXgP7jydR+jS8Aj5PLm/kGSLkfeM7qENPNkzixGl5AqeKwYAAAAAAAAMCnCQQAAAAAAAMCkCAcBAAAAAAAAkyIcBAAAAAAAAEyK7pgAAAAAAABIE0wY5vwYOQgAAAAAAACYFOEgAAAAAAAAYFKEgwAAAAAAAIBJ0XMQAAAAAAAAaYSmg86OkYMAAAAAAACASbnYbDab0UWkp9gEU33cDIvZjDKGZCs/T87Ow43vgDKCe4lWo0vAI3i687OUEVjNdVubIXGPlzH8euee0SXgPyj95mdGl4BHuLuhl9ElOIUrUQlGl5BmnvDzNLqEVMGdLgAAAAAAAGBS9BwEAAAAAABAmmDUuPNj5CAAAAAAAABgUoSDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQTkgAAAAAAACBNMB+J82PkIAAAAAAAAGBShIMAAAAAAACASREOAgAAAAAAACZFz0EAAAAAAACkCReaDjo9Rg4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUvQcBAAAAAAAQJpwEU0HnR0jBwEAAAAAAACTIhwEAAAAAAAATIpwEAAAAAAAADApeg4CAAAAAAAgbdBy0OkxchAAAAAAAAAwKcJBAAAAAAAAwKQIBwEAAAAAAACTIhwEAAAAAAAATIoJSQAAAAAAAJAmmI/E+TFyEAAAAAAAADApwkEAAAAAAADApAgHAQAAAAAAAJOi5yAAAAAAAADShAtNB50eIwcBAAAAAAAAkyIcBAAAAAAAAEyKcBAAAAAAAAAwKXoOAgAAAAAAIE24iKaDzo6RgwAAAAAAAIBJEQ4CAAAAAAAAJkU4CAAAAAAAAJgU4SAAAAAAAABgUkxIAgAAAAAAgLTBfCROj5GDAAAAAAAAgEkRDgIAAAAAAAAmRTgIAAAAAAAAmBQ9BwEAAAAAAJAmaDno/Bg5CAAAAAAAAJgU4SAAAAAAAABgUoSDAAAAAAAAgElliJ6DVqtVrq7kmAAAAAAAABmJC00HnZ7Th4OHDh1STEyMqlSpopw5c8qFf1UAAAAAAABAqnDq4XhBQUEKDw9XlSpVdO/ePUVFRclmsxldFgAAAAAAAJApOG04GBAQoLNnz6p79+7KlSuX3NzcFBMTo8jISAJCAAAAAAAAIBU4ZTgYFBSksLAwdevWzb7O399fFotFsbGxpg0IA44fU7dO7VWneiU1rFdbH0yeqPj4eEnS6VMn1a1Te9WqWlHNXnpB365ba3C1iIyMVIvGLyoo4Lh93cED+/VK25dVq2pFtW/dUnt27TSwQiQnJ6vn6900YcxI+7qvV6/Uy80aqU61Snq5WSN99eVKAyvEX23bukUVy5VS9coV7MuoEUONLsvUoqPvaPyY4WpQt7peqFNNQwb0062bNyRJUydNUK0q5VS3RiX7sn7t1wZXbG6RkZFq3rihAv9yXTp96qS6dGynGlUqqEmj+lr/zRoDKzSvf7vH++rLlWrZtJFqVa2olk0bafWqLwyu1rzOh4XprTdfV92a1dSgbm2NGTlcUVFRDvuc/P6EqlUsZ1CF5vTjhfMaNaCX2jWuo04t6mv6xNGKvpNyXA7t26W+3dur9Ys19Wrbxlq5bIGsVquklL72yxfOUZeXG6pNo1oa0LOLTp0IMvKjZDq5fbx0ZsErqlMmv33dx2/V1p21b+rm6tfty+svlnzgtfWfLaiYdT30VN5s9nUuLtLIDhV1YWln3Vj9mg582ErVivuny2fB/8YlE//JLJwuHAwICNCJEyfsweBfQ8A8efLIy8vLHhCaSVRkpPr37aW27V/R/iOBWrVmnYIDA/Tp0kX6LTpa7/TpqabNW2r/kQCNe3eSZkybojOnTxldtml9HxKi7p1f0eXLl+zrzp0N1aB3+qn9K520/0iARoweq3GjRziEh0hfixd8ou9Dgu1/P7BvrxbMnaPJ0z7SwePBen/qdM3+6EOOkZMIPXNaTZu31LGgE/Zl8tQPjS7L1EYM7q+7cXFav3GHvtu2R25urpr03jhJ0tnQMxo59j3tPxpsX15u297gis3rREiwXu3cweG69Ft0tPr17qlmLVrp4NFATXhvkqZPm6LT3D+kq3+7x9u/b4/mz52tKdNm6HBAiCZ/MF0ff/ShAgOOGV226cTHx6tf7x56tnx57dp/UGs3bFT0nTv2LxhtNpu+XfeN+vR8QwkJCQZXax737sVr7JC+Kln2Wa36bo8WrFin336L1keTx+lC2FlNnzha3Xr01dpthzRx+ifauXWD1n+1QpK0ZcNaHT24R7MWfaE1Ww+q7guNNH5YPyXcu2fwp8ocapTw175prVQ4fw6H9ZWK5lXfTw4ozyvL7MuyHecc9vH3tWjJgOfl5uYYV4xsX0kdniuixmM3KV+n5dpw9Cd9M+Ylebo7XawBZDhO91NksVhUpUoVeyj49wlI8ubNq3z58unChQuKi4szokRD+OXMqV37D6tFq9ZycXFR9J07upeQID+/nNq9a4dy+PqqQ8fOcnd3V9Vq1dW4aXN9vZoRT0b4bsN6jRw+RH3fGeCwfsf2bapQsaJat20nd3d3VaxUWY2bNtear1YbU6jJBR4/pj27dqh+gxft656r97w2bt+tkqVKKykpSXeioiQXF2XLnt3ASnFf6JnTKl26jNFl4A/nzobqzOmTGvfeFGX38ZG3t7dGjXtP/foPVkJCgiIu/KCSpUobXSaUcl0aNXyI+r0z0GH9rp0p9w+v2O8faqhJ0+aMmE5n/3aPV7defW3esVulSpexX5dcXFyUPbuP0WWbzq/XrqpY8RLq2buvPDw85evrpzbtOygkOGWk2YSxo7Tum6/1Vt+3Da7UXG5e/1WFihRTp+695OHhIZ8cvmrSsq3OnAzR9V+vqkmrtqpWq65cXV311NOFVPO5+jpzMkSSdPnnH2Wz2WSz2mSz2eTi4qosWbwM/kSZQ+fni2n54Bc04YtAh/We7q4q8/9yKiT85j++1sVF+nTQC/p0Z5jDeldXF/VrUVaDFh1W+NVoWa02zfz2lFq8u1nme6YQSH1OEw5evHhRklS2bFnlz59fISEhunv37kP3PXXqlLZs2WK6b+W8vVOGVDduUE/tW7dQ7tx51LJVa0WEh6tI0WIO+xYqVFg/nD9vRJmmV7NWbW3cukONGjdxWG9NTpbFYnFY5+rqqp9++jE9y4OkyNu3NXH8GL0/dbq8vBxvAr29vXXxp59Uq0p5+0iOEiVLGVQp7rNarTp3NlQHD+zTSw2eV8P6z+m98WP1W3S00aWZVuiZU3qmUGFtWLdGrZs3UuMGdfTxjGnKnSePLpwPU1JSkhbNn6OX6tdWmxYv6bNPF9sf5UL6Srku7XzguhQRcUFF/37/ULiIfjjv+AsZ0t4/3ePd33bxpx9Vo/KzertPT65LBnn6mUL6ZMFiubm52dft2rHd/iVIn3799fnKr1SSY5OunnjqaU2cMc/huBzau1NFi5dU7XoN1PPtP9uP3LsXr4AjB1WkeMojrE1attO9+Hh1a9NILepX0eeL52r0+zPkmSVLun+OzGbXicsq1etLrT0U4bC+3DO55OHmqnGdKuviZ111al4HDW79rP46Hmhk+0q6GX1Xn+1yvBYVLZBDftmyyNc7iw7PaK3LK7rpu/FNFJ+QrMQk7i+A/5VThIMBAQHaunWrQkNDJaX0FyxYsKDCwsIeCAiDg4N15MgR9e3bV76+vgZUa7xvN2/X9t375ebmqqGD+isuLlYWS1aHfbwsFlONrHQmuXPnkbu7+wPrn3+hgY4eOaxdO7crKSlJ34eEaPvWzbp3L96AKs3LarVq7Khh6tStu4oVL/HQfZ544gkdDjihz79cox3btmj5ssXpXCX+LioyUiVKllKDFxtp/cYt+vyL1fr554v0HDTQb9HRunDhB1269LNWrF6nL75ar5s3rmvCmBGKifldFStXVYeOXbRp+169N2mavl71hVZ+/qnRZZvSP12X4mJjH/jSysvLS3e5fzDM3+/x7iv4xJM6Evi9vli9Rtu3bdHypVyXjGSz2fTJ7Fk6sH+vho4YJUnyz5fP4Kpgs9n02aK5On54v3r1H+6wLS4uVu+NHKAsWbz0coeukqSkpESVrVBZi1dt0LodR9S2c3dNGjNYkbdvGVF+pnL9zl0lWx8cz+eT1VMHzlzVJ5vOqMjrK/X6zD3q06ysBrR6VpJUu3R+daxXVP3mHXjgtTmzpYS2fZqVUcepO1T0jZU6EXFTGyc0lU9Wz7T9QIAJGB4OBgYG6vz58+rdu7d8fHx05swZSVK+fPmUP39+nTt3zh4QBgUF6cCBA+rQoYP8/c3beNTLy0t58vrrnYFDdOTwQVksFsXHO4ao8Xfvytvb26AK8TDlK1TU+1OmaeG8uWpQt5Y+W75ULVq1lo9Pjke/GKnm0yWL5OmZRa906vKP+7h7eMjdw0OlSpfRK526avuWzelYIR4mV+7c+vTzlXq5dVtZLBblL1BAAwcP1aGDBxQbG2N0eabk6ZlyIz5o6Eh5e3srV67c6t1vgI4cOqCyz5bX/MXLVbFyVbl7eKh02XJ6pXM37dyx1eCq8VdeFot90ov74uPjlZX7B8P8/R7v/uhoDw8PeXh4qFTpsurYuZu2btlkcKXmFRMToyED39HmTd9p6fIVKlqsuNElQVJsbIwmjRmsPTs2a9onn+qZwkXt265cuqhBvboqOTlZU2cvUdasKee46RNHq3L1WnriqaeVJYuXOnXvJe9s2XVw7w6jPkamt+fkL2o8dpMOhV5TUrJVQRduau7G02pTu7By+3hpSf/n9frMPfr9buIDr72XlCxJmvRVsC7djFF8QrLGrQhQ9qweqlHSvNlARuHiknmXzMLQcDAgIEDnzp1T164p3948+eSTslgs9hGE+fLlU4ECBXT58mUdPnxYR44cUadOnUwZDJ78PkStmzdWYuKfj1InJCTIw8NDzxQqrIjwcIf9f/wxQoWLFP3728BA0dF3VLhIEa1Zv1H7Dh/XzNmf6Pqvv6oUPdTS1ZZN3ykkKED1alVVvVpVtW3LZm3bsln1alXVyhXLNXKoY0+uxMQE+eQgwDXaD+fDNOuj6Q6TVCUkJMjV1VUeHnxbbIRnChWWzWpVYuKfN/DJ1pQb9317dmnd2q8c9k9ITKCXk5MpUrSYIsIvOKz7MSJcRbh/SFf/do+3bu3XGj7kb9elhATl4LpkiMuXLqnLK20VGxOrlV99QzDoJK7+cln93+ykuNhYzV6yyiEYDDh6UP17dFalarU0acZ8Zff5s1/njeu/OlzDJMnN3V0e7h7pVrvZNK/2tN5o5Dgzsae7q+LvJalBhSeVx9dL341vomsruyvw43aSpMCP22lIm/K68Eu0EpOSlcX9z0fIXVxc5OqSuWaMBYxiWDh49OhRnT9/3j4r8f0+RIULF5aXl5fDCEJJ2rNnj9q3b2/KYFCSihYrrvj4eM2eOUOJiQm6evUXzZoxTa1at1WDFxvp9q1bWrniMyUmJiow4Ji2bt6oli+3Nrps/MWln39W144ddD4spRfX9q1bdGD/XrV/paPRpZnKN99t0f6jQdp3OED7DgfopSZN9VKTptp3OEAVK1XRvj27tXP7VlmtVn1/IkRfrlyhtu1fMbps08uRw1erV63U8mVLlJSUpGtXr2rmjA/VotXL9hFsSF/VqtdUwYJP6P0JoxUXF6uoyEjNn/ux6j7/gixZs2rm9KkKOH5UNptNp06e0FerVqg1sxU7lRcaNNSt27f0xYrl9vuHLZs3qlXrNkaXZir/do9XtXoN7duzSzu2/fW69LnatufeIb39Fh2tnm9017PlK2jeoiXy8/MzuiRI+v233zTynR4qVfZZvf/RfOXw/fO4nDtzShNHDVTPt4eoR7/Bcvtbe4Xqtetq9WeLde2XK0pKStS3X69U1K2bqlrrufT+GKbh4iJNe6OG6pUrKEmqVtxffZuX1ZLt57R6/wXlar9M+TsvV/7Oy1Wl/xpJUpX+azT9m+/1+91EfXUgXB+8UUNP5c0mT3dXvd+tmu7E3NO+078Y+bGATMHF9tdhGOnk2rVr2r17t7p0SXmsL2V2KMe0PyIiQsnJySpWLKVR9t27dx/oi/N/EZuQcecy+jEiXNM/mKzQM2eULXs2NWnaQj3e6iNPT0+dDT2tD6dOVviFH+Tnl1Nv9uqtFq0ybjiYWYbnVihTQouXfabKVatJktZ8tVrLly3WnagoPf1MIb0zcLCqVa9hcJX/dw/rJZLRTBgzMuV/358iSTqwb6/mz/1YV3+5onz5C6j7Gz3UuGlzI0v8n3i4Gd49ItUEBQZo9qyPFH7hB3lmyaKXGjfVwMFDlSUTNA6/l5gxG2nfvHFDs2ZMVUhwoBLuJahO3ec1eNgoZffx0bq1X2nViuW6cf26cuXOrc5du6tth05Gl/x/5umeOX6WypcprsXLPleVP65LoWdOa9rUSfb7hx5v9bFPhJERWdP/tjZV/Ns93v59ezRvTsp1KX/+Aur+Rk81aZZxr0sZ9R5vxWef6qMPP5CXxfLAKKUjgSH2/w4KOK4er7+qE2cy9sQ+v965Z3QJ/8m61Z9r8dwZyuLl9cDvk89WrKqAIweU5W+Tz5UpV1ETZ8zT3bg4fbZojg7t36V7d+/q6SLF9GbfQSpeMuM81VP6zc+MLuGR7m7opRdHf6eDZ65Jkt5oVFLvtCyngrm8dT0qTh9vOKVFW88+8Lqn8mbT+cWdVbzHSl26kdJCxtPdVWM6Vla7OoWVy8dLIeE3NXDhYZ27HJWun+lx3N3Qy+gSnEJUXLLRJaQZv6xuj94pAzAkHIyNjVVERITKlSv3wLa/BoWbNm2S1WpVixYtHhog/p/+vzNwOGgmGfXG0WwyQziY2WWmcDAzy6jhoJlklnAws8uo4aCZ/P/27ve1yvqP4/j7oODGYB36oZNid4wgrG45vCneE/+AxIgRRJB0s/6L+geC/opuKN0oZYjC3HEwmzqbEBL9ICixNWc1TzfCQ7bxDb50znm//Twet9p2IZc840Jeu851+TdeDVXGwdZVGAdbZxz8i3Ewv7H8S3dqaipmZ2djZWUltrcf/5/k0QC4vLwc6+vrcfTo0ce+DwAAAAD8N8b2a/Butxuzs7Oxurq6YyDs9Xpx4cKFOH36dLPPGAQAAACAYRvrZ2R2Gwh7vV4sLCw0+1ZiAAAAABiVvf9+yHB1u92IiFhbW4vNzc04f/58zM/PGwYBAAAAivOUuPxSPF272+3GCy+8EBsbG4ZBAAAAABiRsd85+Mj09HQcO3bMi0cAAAAAYERS3Dn4iGEQAAAAAEYn1TgIAAAAAIxOmo8VAwAAAPBk6YRPiWbnzkEAAAAAaJRxEAAAAAAaZRwEAAAAgEZ55iAAAAAAQ9HxyMH03DkIAAAAAI0yDgIAAABAo4yDAAAAANAozxwEAAAAYCg8cjA/dw4CAAAAQKOMgwAAAADQKOMgAAAAADTKOAgAAAAAjfJCEgAAAACGwxtJ0nPnIAAAAAA0yjgIAAAAAI0yDgIAAABAozxzEAAAAICh6HjoYHruHAQAAACARhkHAQAAAKBRxkEAAAAAaJRnDgIAAAAwFB2PHEzPnYMAAAAA0CjjIAAAAAA0yjgIAAAAAI3yzEEAAAAAhsIjB/Nz5yAAAAAANMo4CAAAAACNMg4CAAAAQKOMgwAAAADQKC8kAQAAAGA4vJEkPXcOAgAAAECjjIMAAAAA0CjjIAAAAAA0yjMHAQAAABiKjocOpufOQQAAAABolHEQAAAAABplHAQAAACARnnmIAAAAABD0fHIwfTcOQgAAAAAjTIOAgAAAECjjIMAAAAA0CjjIAAAAAA0qtPv9/vjPgkAAAAAYPTcOQgAAAAAjTIOAgAAAECjjIMAAAAA0CjjIAAAAAA0yjgIAAAAAI0yDgIAAABAo4yDAAAAANAo4yAAAAAANMo4CAAAAACNMg4CAAAAQKOMg0+Qhw8fjvsU+Bca1aBTfhrVoFMNOuWnUQ065adRDTrB6O0d9wnw37h48WJsbGzE3NxcPP3009HpdMZ9SvyDRjXolJ9GNehUg075aVSDTvlpVINOMB7uHHwCLC0txfr6eszNzcWDBw/i559/jn6/P+7T4m80qkGn/DSqQacadMpPoxp0yk+jGnSC8TEOFre4uBjXr1+Pt956K5555pnYs2dPbGxsxE8//eRCmoRGNeiUn0Y16FSDTvlpVINO+WlUg04wXsbBwpaWluLmzZsxPz8/+N6BAwdicnIyfv31VxfSBDSqQaf8NKpBpxp0yk+jGnTKT6MadILxMw4Wtbi4GMvLy4ML6N8vls8991xMTEwMLqSMh0Y16JSfRjXoVINO+WlUg075aVSDTpCDcbCoycnJmJubG1w8//mg1v3798fMzEx89dVXsbm5OY5TbJ5GNeiUn0Y16FSDTvlpVINO+WlUg06Qg3GwmK+//joiIl599dU4ePBgXL16Ne7fv7/rsSsrK3H27Nn47bffRniGaFSDTvlpVINONeiUn0Y16JSfRjXoBLkYBwtZXFyMc+fOxerqakT89RyG559/Pm7evLnjQtrr9eLSpUvx3nvvRbfbHcPZtkmjGnTKT6MadKpBp/w0qkGn/DSqQSfIxzhYxJUrV2JtbS3OnDkT09PT8eWXX0ZExMzMTBw8eDBu3LgxuJAuLS3FwsJCnDp1Kg4cODDO026KRjXolJ9GNehUg075aVSDTvlpVINOkFOn77U/6S0uLu54e9Pt27dja2srDh8+HBER33//fdy7dy9+/PHH6PV6LqAjplENOuWnUQ061aBTfhrVoFN+GtWgE+TlzsHkLl++HGtra4ML6MOHDyMi4tChQzExMfHYb1oiIr744ot4/fXXXUBHSKMadMpPoxp0qkGn/DSqQaf8NKpBJ8jNnYOJfffdd/H555/Hm2++GRF/vdb9n29vun37dmxvb8dLL70UERH379+PycnJkZ9rqzSqQaf8NKpBpxp0yk+jGnTKT6MadIL83DmY2PT0dLz22muDr/9+AX206R46dChu3boVn376aURETExMjPYkG6dRDTrlp1ENOtWgU34a1aBTfhrVoBPkZxxMbGpqKmZnZ2NlZSW2t7cf+9mjC+ry8nKsr6/H0aNHH/s+o6FRDTrlp1ENOtWgU34a1aBTfhrVoBPk52PFBdy9ezfu3LkThw8fjj179gy+3+v1YmFhId544w3PYhgzjWrQKT+NatCpBp3y06gGnfLTqAadIC93DhbQ7XZjdnY2VldXB79pcQHNRaMadMpPoxp0qkGn/DSqQaf8NKpBJ8jLnYOF3L17N7799tvY3NyM8+fPx/z8vAtoMhrVoFN+GtWgUw065adRDTrlp1ENOkE+xsFi7t27F1evXo2XX37ZBTQpjWrQKT+NatCpBp3y06gGnfLTqAadIBfjYEG7vfqdXDSqQaf8NKpBpxp0yk+jGnTKT6MadII8jIMAAAAA0CgvJAEAAACARhkHAQAAAKBRxkEAAAAAaJRxEAAAAAAaZRwEAAAAgEYZBwEAAACgUXvHfQIAAMNw7dq1+Oyzz3b92VNPPRXvvPNORER8+OGH0el0dhyztbUV7777bjz77LMREXH27Nm4fv36rn/eK6+8EidOnIiIiB9++CE++eST2Ldv347jOp1OvP/++//X3wcAAIbBOAgAPJEePHgQH3zwwa4/W1paGvz38ePH48iRIzuO+eabb+KPP/4YfL1///44efLkv/55v//+e7z99tsxMzPzP48DAIAMfKwYAAAAABplHAQAAACARhkHAQAAAKBRxkEAAAAAaJRxEAAAAAAaZRwEAAAAgEbtHfcJAAAMQ7/fj48++mjXn+3bty+OHDkSERHnzp2LCxcu7Djml19+iTNnzgy+vnPnzq7HRUS8+OKLgz8vIuLjjz+OqampHcdtbW09dhwAAIxbp9/v98d9EgAAAADA6PlYMQAAAAA0yjgIAAAAAI0yDgIAAABAo4yDAAAAANAo4yAAAAAANMo4CAAAAACNMg4CAAAAQKOMgwAAAADQKOMgAAAAADTKOAgAAAAAjTIOAgAAAECjjIMAAAAA0Kg/ATfWKpzTcMcVAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:19.284988Z",
     "start_time": "2025-06-10T04:51:19.282749Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "b26c07559c3a652d",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 决策树",
   "id": "4c2d4fbf9787e0b7"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:26.374238Z",
     "start_time": "2025-06-10T04:51:19.312497Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"\\n训练决策树模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建决策树模型\n",
    "dt_model = DecisionTreeClassifier(\n",
    "    max_depth=20,           # 限制树的最大深度，防止过拟合\n",
    "    min_samples_split=5,    # 分裂内部节点所需的最小样本数\n",
    "    min_samples_leaf=2,     # 叶节点所需的最小样本数\n",
    "    max_features='sqrt',    # 寻找最佳分割时考虑的特征数量\n",
    "    random_state=42,        # 随机种子\n",
    "    class_weight='balanced'  # 平衡类别权重\n",
    ")\n",
    "\n",
    "# 在降维后的训练集上训练模型\n",
    "print(\"开始训练决策树模型...\")\n",
    "dt_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "dt_training_time = time.time() - start_time\n",
    "print(f\"决策树模型训练时间: {dt_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "dt_pred = dt_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"决策树模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "dt_accuracy = accuracy_score(test_labels_encoded, dt_pred)\n",
    "print(f\"\\n决策树测试集准确率: {dt_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\n决策树详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "dt_report = classification_report(test_labels_encoded, dt_pred, target_names=class_names)\n",
    "print(dt_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "dt_conf_matrix = confusion_matrix(test_labels_encoded, dt_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(dt_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.title('决策树模型混淆矩阵')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "16cf29e459b64dcd",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练决策树模型...\n",
      "开始训练决策树模型...\n",
      "决策树模型训练时间: 6.64 秒\n",
      "决策树模型预测时间: 0.01 秒\n",
      "\n",
      "决策树测试集准确率: 0.6103\n",
      "\n",
      "决策树详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.60      0.66      0.63      1999\n",
      "          娱乐       0.49      0.55      0.51      2000\n",
      "          家居       0.53      0.59      0.56      2000\n",
      "          教育       0.74      0.72      0.73      2000\n",
      "          时政       0.47      0.52      0.49      2000\n",
      "          游戏       0.67      0.66      0.67      2000\n",
      "          社会       0.64      0.63      0.63      2000\n",
      "          科技       0.74      0.65      0.69      2000\n",
      "          股票       0.58      0.53      0.55      2000\n",
      "          财经       0.71      0.61      0.65      2000\n",
      "\n",
      "    accuracy                           0.61     19999\n",
      "   macro avg       0.62      0.61      0.61     19999\n",
      "weighted avg       0.62      0.61      0.61     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 20915 (\\N{CJK UNIFIED IDEOGRAPH-51B3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 31574 (\\N{CJK UNIFIED IDEOGRAPH-7B56}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 26641 (\\N{CJK UNIFIED IDEOGRAPH-6811}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\571348178.py:64: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20915 (\\N{CJK UNIFIED IDEOGRAPH-51B3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31574 (\\N{CJK UNIFIED IDEOGRAPH-7B56}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26641 (\\N{CJK UNIFIED IDEOGRAPH-6811}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 朴素贝叶斯",
   "id": "b1a7c70a07355610"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:26.801725Z",
     "start_time": "2025-06-10T04:51:26.394348Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"\\n训练朴素贝叶斯模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建多项式朴素贝叶斯模型 - 适合处理词频特征\n",
    "nb_model = MultinomialNB(\n",
    "    alpha=0.1,  # 平滑参数，较小的值意味着较少的平滑\n",
    "    fit_prior=True  # 是否学习类别先验概率\n",
    ")\n",
    "\n",
    "# 在训练集上训练模型\n",
    "print(\"开始训练朴素贝叶斯模型...\")\n",
    "nb_model.fit(X_train_tfidf, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "nb_training_time = time.time() - start_time\n",
    "print(f\"朴素贝叶斯模型训练时间: {nb_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "nb_pred = nb_model.predict(X_test_tfidf)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"朴素贝叶斯模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "nb_accuracy = accuracy_score(test_labels_encoded, nb_pred)\n",
    "print(f\"\\n朴素贝叶斯测试集准确率: {nb_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\n朴素贝叶斯详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "nb_report = classification_report(test_labels_encoded, nb_pred, target_names=class_names)\n",
    "print(nb_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "nb_conf_matrix = confusion_matrix(test_labels_encoded, nb_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(nb_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Naive Bayes Confusion Matrix')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "6cd46417cd9f36f9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练朴素贝叶斯模型...\n",
      "开始训练朴素贝叶斯模型...\n",
      "朴素贝叶斯模型训练时间: 0.09 秒\n",
      "朴素贝叶斯模型预测时间: 0.01 秒\n",
      "\n",
      "朴素贝叶斯测试集准确率: 0.8659\n",
      "\n",
      "朴素贝叶斯详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.88      0.93      0.90      1999\n",
      "          娱乐       0.81      0.89      0.85      2000\n",
      "          家居       0.85      0.86      0.86      2000\n",
      "          教育       0.92      0.88      0.90      2000\n",
      "          时政       0.80      0.84      0.82      2000\n",
      "          游戏       0.86      0.88      0.87      2000\n",
      "          社会       0.87      0.88      0.88      2000\n",
      "          科技       0.96      0.81      0.88      2000\n",
      "          股票       0.85      0.82      0.83      2000\n",
      "          财经       0.88      0.86      0.87      2000\n",
      "\n",
      "    accuracy                           0.87     19999\n",
      "   macro avg       0.87      0.87      0.87     19999\n",
      "weighted avg       0.87      0.87      0.87     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "D:\\an\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 比较各个模型的训练时间",
   "id": "4330ffd287e75d15"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:57.863001Z",
     "start_time": "2025-06-10T04:51:57.579191Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 收集所有模型的训练时间\n",
    "models = ['Logistic Regression', 'SVM', 'Random Forest', 'Decision Tree', 'Naive Bayes']\n",
    "training_times = [log_training_time, svm_training_time, rf_training_time, dt_training_time, nb_training_time]\n",
    "accuracies = [log_accuracy, svm_accuracy, rf_accuracy, dt_accuracy, nb_accuracy]\n",
    "# 创建DataFrame以便于绘图和分析\n",
    "performance_df = pd.DataFrame({\n",
    "    'Model': models,\n",
    "    'Training Time (s)': training_times,\n",
    "    'Accuracy': accuracies\n",
    "})\n",
    "# 创建一个包含训练时间和准确率的双轴图表\n",
    "fig, ax1 = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# 设置第一个Y轴（训练时间）\n",
    "color = 'tab:blue'\n",
    "ax1.set_xlabel('Model', fontsize=12)\n",
    "ax1.set_ylabel('Training Time (seconds)', color=color, fontsize=12)\n",
    "bars = ax1.bar(performance_df['Model'], performance_df['Training Time (s)'], color=color, alpha=0.7)\n",
    "\n",
    "# 添加训练时间数据标签\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    ax1.text(bar.get_x() + bar.get_width()/2., height + 0.5,\n",
    "             f'{height:.2f}s', ha='center', va='bottom', fontsize=10, color=color)\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "ax1.set_xticklabels(performance_df['Model'], rotation=45, ha='right')\n",
    "\n",
    "# 创建第二个Y轴（准确率）\n",
    "ax2 = ax1.twinx()\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Accuracy', color=color, fontsize=12)\n",
    "ax2.plot(performance_df['Model'], performance_df['Accuracy'], 'o-', color=color, linewidth=2, markersize=8)\n",
    "\n",
    "# 添加准确率数据标签\n",
    "for i, acc in enumerate(performance_df['Accuracy']):\n",
    "    ax2.text(i, acc + 0.01, f'{acc:.4f}', ha='center', va='bottom', fontsize=10, color=color)\n",
    "\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "ax2.set_ylim([0, 1.05])  # 设置准确率轴的范围\n",
    "\n",
    "# 添加标题和网格\n",
    "plt.title('Model Performance: Training Time vs. Accuracy', fontsize=15)\n",
    "ax1.grid(axis='y', linestyle='--', alpha=0.3)\n",
    "fig.tight_layout()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "# 打印性能对比表格\n",
    "print(\"\\n模型性能对比:\")\n",
    "performance_df['Accuracy'] = performance_df['Accuracy'].apply(lambda x: f\"{x:.4f}\")\n",
    "performance_df['Training Time (s)'] = performance_df['Training Time (s)'].apply(lambda x: f\"{x:.2f}\")\n",
    "print(performance_df)"
   ],
   "id": "83c2f895ff1e6193",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_10800\\1252378150.py:31: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax1.set_xticklabels(performance_df['Model'], rotation=45, ha='right')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能对比:\n",
      "                 Model Training Time (s) Accuracy\n",
      "0  Logistic Regression             16.11   0.8319\n",
      "1                  SVM             54.94   0.8254\n",
      "2        Random Forest             57.57   0.7961\n",
      "3        Decision Tree              6.64   0.6103\n",
      "4          Naive Bayes              0.09   0.8659\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:26.869197Z",
     "start_time": "2025-06-10T04:51:26.867265Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "322a240556ff39a6",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:26.894513Z",
     "start_time": "2025-06-10T04:51:26.891422Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "75d4ff2edcdf4265",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:26.915432Z",
     "start_time": "2025-06-10T04:51:26.913104Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "49a86985df948d03",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:26.941591Z",
     "start_time": "2025-06-10T04:51:26.939059Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "510111d99ec46b4",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:26.977808Z",
     "start_time": "2025-06-10T04:51:26.974310Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "6c0b390c8059d775",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-10T04:51:27.007748Z",
     "start_time": "2025-06-10T04:51:27.004243Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "a4019bf19ac625a9",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
